What this article is about
In brief
In a study of 446 Chinese neuro-oncology nurses, 36. 4% reported missed nursing care, and latent profile analysis identified three distinct risk groups.
For nursing students
Study summary
Missed nursing care (MNC) happens when necessary nursing tasks — like repositioning a patient, monitoring vital signs, or educating a family — are delayed, skipped entirely, or only partly completed. On a neuro-oncology unit, where patients may have seizures, cognitive changes, or complex symptom burdens from brain tumors, missed care can have serious consequences. This study, published in BMC Nursing in April 2025, asked a focused question: instead of treating all nurses as facing the same level of risk for missed care, could researchers identify distinct "types" of nurses based on their patterns of missed care, and then figure out what predicts which type a nurse falls into?
The researchers surveyed 446 neuro-oncology nurses working in 10 large ("Grade A") oncology hospitals across six provinces in China, between April and June 2024. To take part, nurses needed at least one year of clinical experience and had to be actively providing direct patient care. Participants completed four tools: a general information questionnaire (age, education, job satisfaction, and similar background details); the Oncology Missed Nursing Care Self-Rating Scale, which asks nurses to rate how often specific care activities are missed across four areas (assessment, care planning, primary/basic care, and nursing interventions); the Practice Environment Scale, which measures things like staffing adequacy and quality of nursing leadership; and the Psychological Capital Scale, which measures internal resources such as self-confidence and resilience.
Rather than simply averaging everyone's missed-care scores, the researchers used a statistical method called latent profile analysis (LPA). LPA looks for hidden ("latent") subgroups within a population based on patterns across multiple measured variables — in this case, the different domains of missed care. This approach assumes that not all nurses experience missed care the same way, and it tries to sort people into naturally occurring groups rather than forcing everyone onto a single scale.
Overall, 36.4% of nurses reported experiencing missed nursing care. The analysis identified three distinct profiles. The "severe missed nursing care" profile (about 21% of nurses) had the lowest scores across every domain, with especially poor performance on nursing assessment tasks. The "medium-risk" profile was the largest group, making up just over half of all nurses, with moderate missed-care scores. The "low-risk" profile, representing just over a quarter of nurses, had the highest scores, reflecting strong follow-through on assessments, planning, and interventions.
The researchers then used a technique called multinomial logistic regression to see which nurse characteristics predicted which profile someone belonged to, using the severe profile as the comparison group. Nurses aged 18–35 were far more likely to land in the low-risk (best-performing) group than older nurses. Nurses with only a technical secondary school or junior college education (rather than a bachelor's degree or higher) were more likely to be in the medium-risk group than the severe group — a somewhat unexpected finding the authors suggest may reflect earlier entry into frontline clinical work. Nurses who reported greater job satisfaction and mental resilience were also more likely to fall into the medium-risk group rather than the severe group; the study's self-confidence measure, interestingly, pointed in the opposite direction, so that particular result should be read with caution. Finally, nurses working in units with better practice environments — meaning adequate staffing, supportive leadership, and good interprofessional collaboration — were more likely to be in either the medium-risk or low-risk groups.
For nursing students, this study is a useful example of how research can move beyond "how much care is missed on average" toward asking "are there different types of nurses at different levels of risk, and what protects some of them?" It also highlights two levers hospitals can act on directly: the practice environment (staffing, leadership, teamwork) and nurses' psychological resources (confidence and resilience), both of which showed protective associations with lower rates of missed care. As you think about your own future practice environment, this study is a reminder that missed care is rarely just about individual effort — it is shaped by the conditions nurses work within.
Original publication
Source abstract and study details
Read the source abstract
Abstract Purpose To explore potential profile characteristics associated with neuro-oncology nurses’ missed nursing care (MNC) and analyze differences in characteristics of neuro-oncology nurses across these profiles. Methods A cross-sectional study design using convenience sampling involved 446 neuro-oncology nurses from ten Grade A oncology hospitals across six provinces in China, conducted from April to June 2024. The General Information Questionnaire, the Oncology Missed Nursing Care Self-Rating Scale, the Practice Environment Scale, and the Psychological Capital Scale were employed for data collection. Latent profile analysis was performed to identify MNC profiles, followed by multinomial logistic regression analysis to examine predictors of MNC. Results The incidence of MNC among neuro-oncology nurses was found to be 36.4%. Three latent profiles were identified: “severe missed nursing care profile” (20.6%), “medium-risk missed nursing care profile” (51.3%), and ‘low-risk missed nursing care profile’ (28.1%). Compared with the “severe missed nursing care profile,” neuro-oncology nurses with a technical secondary school or junior college education, who expressed job satisfaction, good self-confidence, and mental resilience were more likely to fall into the “medium-risk missed nursing care profile.” Additionally, those aged 18–35 years were more likely to be categorized in the “low-risk missed nursing care profile,” and nurses working in a positive nursing work environment were also more likely to belong to the “medium-risk” or “low-risk missed nursing care profiles.” Conclusion There is notable heterogeneity in the levels of missed nursing care among neuro-oncology nurses. Nursing managers should prioritize addressing middle-risk missed nursing care and enhancing both the working environment and psychological support for neuro-oncology nurses. Tailored interventions based on the distribution of different profiles can improve nursing quality, increase job satisfaction, and enhance patient outcomes.
Reviewed findings
Main findings
- Among 446 neuro-oncology nurses across 10 Chinese oncology hospitals, 36.4% reported missed nursing care overall.
- Latent profile analysis identified three distinct MNC subgroups: a severe profile (20.6%), a medium-risk profile (51.3%, the largest group), and a low-risk profile (28.1%).
- Nurses aged 18–35 had substantially lower odds of falling into the severe missed-care profile compared with the low-risk profile (OR=0.151, p<0.001).
- A better practice environment (adequate staffing, strong leadership, good collaboration) was significantly protective, predicting membership in both the medium-risk profile (OR=1.034) and low-risk profile (OR=1.033) over the severe profile.
- Greater job satisfaction (OR=2.577, p=0.042) and higher mental resilience (OR=1.150, p=0.039) were associated with higher odds of medium-risk rather than severe profile membership; self-confidence was also a significant predictor (OR=0.851, p=0.006), but in the opposite direction (lower odds of medium versus severe), which sits in tension with the abstract's plain-language summary and should be read cautiously.
Using the findings
Practice considerations
- Nurse managers can use practice-environment improvements — staffing adequacy, visible leadership, and interprofessional collaboration — as a concrete, modifiable lever to reduce the odds of severe missed-care patterns on a unit.
- Because the medium-risk profile was the largest group (over half of nurses), targeted interventions aimed at this middle tier may have the greatest overall impact on reducing unit-wide missed care, rather than focusing resources only on the most severe cases.
- Programs that build psychological capital — particularly resilience — may be worth piloting as a complement to staffing fixes, given resilience's measured association with lower-risk missed-care profiles in this sample; the self-confidence result was less clear-cut and should not on its own justify a self-confidence-focused intervention.
- Because younger nurses (18–35) were more likely to be in the low-risk group, newer graduates should not automatically be assumed to be at higher risk for missed care; onboarding and mentorship efforts might instead focus on identifying which specific nurses, regardless of age, show early signs of the severe profile's assessment gaps.
- The finding that technical/junior-college-educated nurses showed lower odds of the severe profile (versus bachelor's-prepared peers) should prompt further local-context evaluation rather than being applied directly to hiring or staffing decisions, since the study offers no causal explanation for this pattern.
For educators
Teaching and appraisal notes
This multicenter cross-sectional study (BMC Nursing, April 2025) examined missed nursing care (MNC) among 446 neuro-oncology nurses across 10 Grade A oncology hospitals in six Chinese provinces, using a person-centered rather than variable-centered analytic strategy. This distinction is worth foregrounding for students: instead of asking whether predictors correlate with an overall missed-care score, the authors used latent profile analysis (LPA) in Mplus 8.3 to identify subgroups of nurses who share similar missed-care patterns across four domains (assessment, care planning, primary care, and nursing interventions), then used multinomial logistic regression in SPSS 27.0 to test which characteristics predict subgroup membership.
Instrumentation is a strong point for classroom discussion: the Oncology Missed Nursing Care Self-Rating Scale (33 items, four domains, Cronbach's α = 0.95), the Practice Environment Scale (31 items, α = 0.91), and the Psychological Capital Scale (30 items, α = 0.91) all show strong internal consistency, and model selection for the LPA drew on standard fit indices (AIC, BIC, entropy, likelihood ratio tests) — this offers a concrete opportunity to teach how researchers justify a chosen number of latent classes rather than assuming one arbitrarily.
The headline results: overall MNC incidence was 36.4%, and three profiles emerged — severe (20.6%, n=92, lowest scores across all domains and notably poor on assessment), medium-risk (51.3%, n=229, the largest and modal group), and low-risk (28.1%, n=125, highest scores). Using the severe profile as the reference category, several predictors were statistically significant: age 18–35 sharply reduced the odds of severe-profile membership relative to low-risk (OR=0.151, p<0.001); practice environment score was protective for both medium-risk (OR=1.034, p<0.001) and low-risk (OR=1.033, p<0.001) membership; job satisfaction (OR=2.577, p=0.042) and technical/junior-college education (OR=2.807, p=0.011) increased odds of medium- versus severe-profile membership; and mental resilience showed a positive association with medium-risk relative to severe (OR=1.150, p=0.039). Self-confidence was a significant predictor of medium- versus severe-profile membership (OR=0.851, p=0.006) but in the opposite (negative) direction to the other psychological-capital variables; this sits in tension with the abstract, which groups self-confidence among factors favouring the medium-risk profile, and is worth flagging to students as an abstract-versus-table discrepancy warranting cautious interpretation.
The education finding is worth flagging as a discussion point rather than a straightforward "protective factor": the authors themselves note it counters intuition, and they offer only a speculative explanation (earlier clinical entry, greater hands-on time) rather than a tested mechanism. This is a good moment to model appraisal skills — distinguishing a statistically significant association from a causal or even fully understood relationship.
Methodologically, the cross-sectional design cannot establish causality or temporal order between psychological capital, practice environment, and missed care — all three could plausibly influence each other bidirectionally. Convenience sampling from tertiary oncology centers limits generalizability to community or non-oncology settings, and the sample skewed heavily female (~93%), limiting insight into any gender-related patterns. The self-report nature of the missed-care measure is also a standard limitation of this literature.
For teaching, this article pairs well with units on person-centered analytic methods (LPA versus regression-only approaches), nursing practice environment theory, and psychological capital in the workplace. It also supports discussion of how findings that seem to challenge assumptions (e.g., education level) should prompt more cautious interpretation rather than immediate practice change. The authors' own conclusion — that middle-risk nurses represent the largest intervention target, and that both environmental and psychological levers matter — offers a concrete framework for translating LPA findings into targeted, tiered staff-support strategies rather than one-size-fits-all interventions.
Critical appraisal
Limitations
- The cross-sectional design cannot establish causality or determine the direction of relationships among psychological capital, practice environment, and missed nursing care.
- Convenience sampling was restricted to tertiary ("Grade A") oncology hospitals in China, limiting generalizability to community hospitals, non-oncology units, or other health systems.
- The sample was approximately 93% female, leaving too few male nurses to examine gender-related patterns in missed care.
Classroom use
Discussion Questions
- How does latent profile analysis differ conceptually from simply comparing group averages on a missed-care scale, and what does that difference let researchers see that a traditional regression approach might miss?
- Why might the largest subgroup (the medium-risk profile, at 51.3%) be a more strategically important target for intervention than the smallest, most severe subgroup?
- What are some possible explanations, beyond the authors' speculation about earlier workforce entry, for why nurses with technical/junior-college education showed better outcomes than bachelor's-prepared peers in this sample?
- How might a nurse manager operationalize "practice environment" improvements — staffing adequacy, leadership, and collaboration — on a real neuro-oncology unit, and how would they measure whether those changes reduced missed care?
- Given that psychological capital (confidence, resilience) showed protective associations, what would an evidence-informed staff-support program targeting these traits look like, and how would you evaluate whether it changed missed-care patterns rather than just self-reported mood?
- Why can't this cross-sectional design tell us whether a poor practice environment causes missed care, or whether nurses who already provide less thorough care perceive their environment more negatively?
Source-based questions
Frequently asked questions
What is latent profile analysis and why did researchers use it here?
Latent profile analysis is a person-centered statistical method that groups individuals into hidden subgroups based on shared patterns across multiple variables, rather than treating a population as uniform. Researchers used it to reveal that neuro-oncology nurses experience missed care in distinct patterns rather than along a single continuum.
Why did nurses with lower educational attainment show better outcomes in this study?
Nurses with technical secondary or junior-college education had higher odds of the medium-risk versus severe profile compared with bachelor's-prepared nurses. The authors describe this as counterintuitive and offer only a speculative explanation (possibly earlier clinical workforce entry), so it should be interpreted cautiously rather than applied directly to practice.
What can hospitals do based on these findings to reduce missed nursing care?
The study suggests improving the practice environment (staffing adequacy, leadership, collaboration) and supporting nurses' psychological capital (confidence, resilience), with particular attention to the largest, medium-risk group of nurses.
What are the main limitations of this study?
It is cross-sectional (no causal conclusions), used convenience sampling limited to tertiary oncology hospitals in China, was about 93% female, and relied on nurse self-report rather than direct observation of care.