Perceived Importance of Professional Competencies for Admission to the College of Kinesiologists of Ontario
Bibliographic record
Abstract
Background: For more than a decade, the development of professional competencies has been at the heart of curriculum reforms in Education and Health care training worldwide. Reference documents of competencies have been developed in Australia (Brownie, Thomas, McAllister, & Groves, 2014), Canada (Boucher & Ste-Marie, 2013; Royal College of Physicians and Surgeons of Canada, 2005), United-States (Hurd & Buschbom, 2010), and in Europe (Battel-Kirk, Van der Zanden & al., 2012). In most of these reference documents, the number of professional competencies that a professional must acquire does not exceed twenty. However, in the field of kinesiology, the College of Kinesiologists of Ontario (CKO) (2013) has defined 54 professional competencies required for admittance to the College. Objective: The purpose of this research project aims to identify, from the standpoint of university instructors who teach in a kinesiology program, the professional competencies that are considered the 17 most relevant from those suggested by CKO. Methodology: Participants (N=23) were required to complete an online survey on the domain www.SurveyMonkey.com in order to determine the competencies deemed most important by them. Results: The quantitative data obtained through the use of the surveys allowed for a list of 17 competencies to be retained at the end of the study. The qualitative data provided by the participants supported and complemented the quantitative data. Conclusion: The competencies retained in this study undoubtedly represented the views of all the participants in regards to the essential competencies that a kinesiologist should be able to demonstrate upon admission to the College of Kinesiologists of Ontario. Keywords: Professional competencies, Curriculum, Kinesiology
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".