French translation and validation of the Readiness for Interprofessional Learning Scale (RIPLS) in a Canadian undergraduate healthcare student context
Bibliographic record
Abstract
The Canadian Interprofessional Health Collaborative recommends that future professionals be prepared for collaborative practice. To do so, it is necessary for them to learn about the principles of interprofessional collaboration. Therefore, to ascertain if students are predisposed, their attitude toward interprofessional learning must be assessed. In the French Canadian context such a measuring tool has not been published yet. The purpose of this study is to translate in French an adapted version of the RIPLS questionnaire and to validate it for use with undergraduate students from seven various health and social care programmes in a Canadian university. According to Vallerand's methodology, a method for translating measuring instruments: (i) the forward-backward translation indicated that six items of the experimental French version of the RIPLS needed to be more specific; (ii) the experimental French version of the RIPLS seemed clear according to the pre-test assessing items clarity; (iii) evaluation of the content validity indicated that the experimental French version of the RIPLS presents good content validity and (iv) a very good internal consistency was obtained (α = 0.90; n = 141). Results indicate that the psychometric properties of the RIPLS in French are comparable to the English version, although a different factorial structure was found. The relevance of three of the 19 items on the RIPLS scale is questionable, resulting in a revised 16-item scale. Future research aimed at validating the translated French version of the RIPLS could also be conducted in another francophone cultural context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".