Empowering chiropractic students: the CHIRO-Force peer-assisted learning programs for manual therapy education
Bibliographic record
Abstract
This dataset presents information related to the evaluation of the pilot year of the CHIRO-Force peer mentoring program, aimed at learning manual therapies using detection technology at the Université du Québec à Trois-Rivières. Two mentors (final-year students in the chiropractic program) were recruited in Spring 2025. The program was launched in September 2023 with fifth-year students and then expanded to include first-year students in December 2023. The dataset includes: (1) information about students who participated in at least one mentoring session and agreed to take part in the program evaluation; (2) post-session evaluations that mentees were invited to complete; (3) the end-of-pilot-year program evaluation that mentees were invited to complete. The data collected are both qualitative and quantitative in nature. Ce jeu de données présente les informations liées à l’évaluation de l’année pilote du programme de mentorat par les pairs CHIRO-Force, destiné à l’apprentissage des thérapies manuelles avec technologie de détection à l’Université du Québec à Trois-Rivières. Deux mentors (étudiants en dernière année du programme de chiropratique) ont été recrutés au printemps 2025. Le programme a été lancé en septembre 2023 avec les étudiants de cinquième année, puis élargi aux étudiants de première année en décembre 2023. Le jeu de données comprend : (1) les informations concernant les étudiants ayant participé à au moins une séance de mentorat et ayant accepté de prendre part à l’évaluation du programme ; (2) les évaluations post-séance que les mentorés étaient invités à remplir ; (3) l’évaluation de fin d’année pilote du programme que les mentorés étaient invités à compléter. Les données recueillies sont de nature qualitative et quantitative.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".