Interprofessional collaboration amongst health care professionals in Federal Medical Centre, Owo, Ondo State, Nigeria: A cross-sectional study
Bibliographic record
Abstract
Objective: Effective interprofessional collaboration (IPC) is a cornerstone of high-quality healthcare delivery. Given the complexity of healthcare systems, optimal patient outcomes depend on the ability of professionals across disciplines to work cohesively. Conversely, weak collaboration among health workers contributes to poor service quality, a challenge evident in Nigeria. This study explored the perceptions and practices of IPC among healthcare professionals at the Federal Medical Centre, Owo, Ondo State, Nigeria. Methods: A cross-sectional survey was conducted using the validated Assessment of Interprofessional Collaboration Scale questionnaire, rated on a five-point Likert scale. Data analysis involved mean ± standard deviation for continuous variables and proportions/percentages for categorical data. Results: A total of 185 respondents participated, with the majority (77.3%) aged between 20–39 years. Females accounted for 61.1% of the sample, though gender distribution varied by profession: nursing remained predominantly female (91.2%), while medical laboratory science was male-dominated (87.5%). Among specialists, laboratory scientists (37.5%), physiotherapists (35%), and doctors (33.3%) had the highest proportions. Doctors and nurses recorded the highest mean scores across most IPC domains, particularly role clarity (4.40 and 4.33, respectively) and trust (4.45 and 4.34). Administrators and “others” consistently recorded the lowest scores (3.12–3.74). ANOVA revealed significant differences across all parameters (p < .001). Post-hoc analysis confirmed stronger doctor–nurse collaboration compared to other groups. Conclusions: Findings revealed that doctors demonstrated the strongest interprofessional collaboration, followed by nurses and pharmacists. In contrast, physiotherapists, laboratory scientists, administrators, and other cadres reported lower levels of collaboration. Notably, doctors consistently rated themselves highly and were similarly rated by other professional groups. Strengthening IPC across all healthcare professions remains essential to improving teamwork and ensuring better patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".