Processus de validation du questionnaire IPC65 : un outil de mesure de l'interdisciplinarité en pratique clinique
Bibliographic record
Abstract
AIM: Interdisciplinary clinical practice has become an essential objective for the management of complex cases in a large number of health facilities in Quebec and elsewhere. However, this highly desirable practice cannot be implemented on demand and requires a carefully designed approach in combination with continuous feedback between the various partners involved in the management and functioning of an interdisciplinary team. The purpose of this research was to provide teams with a tool to help them identify their strengths and weaknesses in order to ensure continuous improvement. METHODS: Following a comprehensive review of the literature on microsystems ensuring interdisciplinarity in health, we identified a large number of elements considered to be important factors allowing effective interdisciplinarity. These factors were used to construct a questionnaire that was submitted to several stages of validation (qualitative and statistical) designed to enable health professionals to measure their degree of integration of the concepts allowing interdisciplinary clinical practice. RESULTS: This approach allowed validation of this questionnaire (Cronbach's alpha greater than 0.97). During the validation process, the number of questions of the questionnaire was reduced from 99 to 65. CONCLUSION: The various steps of validation of the questionnaire allowed the development of a relevant tool to promote continuous improvement of interdisciplinary clinical teams.
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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 | 0.130 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".