How are exercise professionals described within academic literature? A narrative review
Bibliographic record
Abstract
There is growing interest to include exercise into healthcare for the prevention, treatment, and management of chronic disease. However, what defines an "exercise professional" to fulfil these roles and responsibilities is not clear across Canada. While exercise science often integrates exercise professionals within academic studies to assist exercise-based research, exploring how exercise professionals are involved in these contexts may contribute to developing a clearer understanding of how these professionals may be characterized. The purpose of this narrative review was to summarize how exercise professionals are described in research and identify the components that inform their unique roles. A keyword search of "exercise professional" was performed in multiple databases, including Medline, SportDiscus, and Web of Science to identify peer-reviewed studies that included "exercise professionals". Fifteen studies were included, and two independent reviewers performed a reflexive thematic analysis to derive three themes that represent how exercise professionals were described: (1) job title; (2) roles/duties (e.g., fitness testing, motivational interviewing), and (3) education/qualifications/credentials. Common occupations involved in research included exercise physiologists, kinesiologists, and personal trainers. Exercise professionals were responsible for interacting with clinical and non-clinical populations in a variety of contexts. Similarly, qualifications varied, with some studies specifying further training to work with special populations (e.g., patients who have cancer). By identifying these discrepancies, this study highlights the need to develop consistent definitions and roles to promote recognition and integration of exercise professionals in both clinical and community settings.
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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".