Assessing Interprofessional Learning during a Student Placement in an Interprofessional Rehabilitation University Clinic in Primary Healthcare in a Canadian Francophone Minority Context
Bibliographic record
Abstract
Background: Interprofessional collaboration is deemed the key to quality patient care and the future for healthcare delivery models. Such a complex competency needs to be learned; as such, interprofessional education should be a key component of health professional programs. An Interprofessional Rehabilitation University Clinic was created to promote interprofessional education at the pre-licensure level. However, few resources are currently available to assess interprofessional learning; no tool (English or French) that specifically assesses interprofessional learning could be identified.Methods and Findings: A self-administered questionnaire was developed to assess interprofessional learning during a clinical placement. Using a single-group posttest-only design, this descriptive pilot project reports the results obtained with this tool for the first 15 students on placement at the Clinic. Preliminary findings suggest this tool helped demonstrate that, during placements in an interprofessional clinic, students developed some understanding of their own profession as well as of other professions. Responses showed that participants believe that interprofessional interventions are more efficient, save time, and facilitate sharing of information leading to a better comprehension of the clients’ situations. The tool suggests that students feel that an interprofessional educational experience is beneficial for clients and for themselves.Conclusions: Assessing interprofessional learning is challenging. Although the tool developed during this project is most promising, further research is warranted to increase its usefulness in assessing interprofessional learning.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".