Performance indicators for manual therapy skills required of chiropractic students at the start of their senior internship: a modified Delphi study
Bibliographic record
Abstract
This dataset presents information related to a modified Delphi study conducted to identify key performance indicators in chiropractic education. The goal of the study was to reach expert consensus on which clinical behaviors junior interns should demonstrate before entering their senior internship. Experts were recruited from four stakeholder groups: students, recent graduates, faculty members, and clinical supervisors. An initial list of 124 performance indicators was refined through three rounds of online surveys conducted between June and November 2023. Experts rated each indicator, provided qualitative feedback, and helped prioritize educational strategies. Between 26 and 30 participants completed each round. The final dataset includes: (1) the initial and final lists of performance indicators; (2) expert ratings and comments for each round; (3) the subset of indicators that reached consensus as critical priorities for curriculum development. The data are both qualitative and quantitative in nature, and provide a foundation for improving student readiness, clinical competence, and patient safety in chiropractic education.// Ce jeu de données présente les informations liées à une étude Delphi modifiée visant à identifier les principaux indicateurs de performance en formation chiropratique. L’objectif de l’étude était d’obtenir un consensus d’experts sur les comportements cliniques que les stagiaires juniors doivent démontrer avant d’entreprendre leur internat senior. Des experts issus de quatre groupes de parties prenantes ont été recrutés : étudiants, diplômés récents, membres du corps professoral et superviseurs cliniques. Une liste initiale de 124 indicateurs de performance a été raffinée au cours de trois rondes de sondages en ligne menées entre juin et novembre 2023. Les experts ont évalué chaque indicateur, formulé des commentaires qualitatifs et identifié des priorités pour orienter les stratégies pédagogiques. Entre 26 et 30 participants ont complété chaque ronde. Le jeu de données final comprend : (1) les listes initiale et finale des indicateurs de performance ; (2) les évaluations et commentaires des experts pour chaque ronde ; (3) le sous-ensemble d’indicateurs ayant atteint un consensus comme priorités critiques pour le développement curriculaire. Les données recueillies sont de nature à la fois qualitative et quantitative, et constituent une base pour améliorer la préparation des étudiants, la compétence clinique et la sécurité des patients en formation chiropratique. // Ethical considerations prevent this dataset from being publicly available. The participants did not consent to having their depersonalized data shared openly. You may click on the “Contact owner” button to request access under specific conditions. // Des considérations éthiques empêchent la mise à disposition publique de ce jeu de données. En effet, les participantes et participants n'ont pas consenti à ce que leurs données dépersonnalisées soient rendues accessibles publiquement. Vous pouvez cliquer sur « Contacter le ou la propriétaire » pour en faire la demande, sous certaines conditions.
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 | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".