Attitudes towards interprofessional collaboration among primary care physicians and nurses in Singapore
Bibliographic record
Abstract
Interprofessional collaboration (IPC) has been shown to improve patient outcomes, cost efficiency, and health professional satisfaction, and enhance healthy workplaces. We determined the attitudes of primary care physicians and nurses towards IPC and factors facilitating IPC using a cross-sectional study design in Singapore. A self-administered anonymous questionnaire, based on the Jefferson Scale of Attitudes toward Physician-Nurse Collaboration (JSAPNC), was distributed to primary healthcare physicians and nurses working in National Healthcare Group Polyclinics (N = 455). We found that the mean JSAPNC score for physicians was poorer than that for nurses (50.39 [SD = 4.67] vs. 51.61 [SD = 4.19], respectively, mean difference, MD = 1.22, CI = 0.35-2.09, p = .006). Nurses with advanced education had better mean JSAPNC score than nurses with basic education (52.28 [SD = 4.22] vs. 51.12 [SD = 4.11], respectively, MD = 1.16, CI = 0.12-2.20, p = .029). Male participants had poorer mean JSAPNC score compared to females (50.27 [SD = 5.02] vs. 51.38 [SD = 4.22], respectively MD = 1.11, CI = 0.07-2.14, p = .036). With regression analysis, only educational qualification among nurses was independently and positively associated with JSAPNC scores (p = .018). In conclusion, primary care nurses in Singapore had more positive attitudes towards IPC than physicians. Among nurses, those with advanced education had more positive attitudes than those with basic education. Greater emphasis on IPC education in training of physicians and nurses could help improve attitudes further.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".