Critical Health Communication in Kinesiology and Sports Science
Bibliographic record
Abstract
Fields neighboring the disciplines of kinesiology and sports science have called for more interdisciplinary work, including the adoption of critical approaches to research. This scoping review explored the degree to which critically-aligned research has developed within these disciplines. The goal was to identify who this research studied, what methods were used, and which theoretical and conceptual frameworks were adopted. Publications between 2010-2022 in six top kinesiology and sports science journals using four databases were searched using keywords to identify critically-aligned research. A multi-step screening process was used to identify and sort articles that adequately fit the criteria of critically-aligned research. The scoping review identified 5666 entries of which 3300 were unique publications. 76 articles were assessed to be critically-aligned. Four themes regarding demographics emerged: Geographic area, gender, race/ethnicity/indigeneity, and inequality/inequity. Regarding methodology, three major theoretical and conceptual frameworks emerged: ecological, socio-economic, and cultural. Overall, a relatively small number of studies fit our search criteria, suggesting that critically-aligned research remains at the margins of the disciplines. For the studies that were critically-aligned, they often centered the Global North and were inconsistent in their application of categories such as race, ethnicity, inequality and equity. These studies were diverse in their methodological approach while relying on ecological, socio-economic, and cultural frameworks. To heed the calls for a more interdisciplinary approach, and to advance the disciplines more generally, kinesiology and sports science should expand their adoption of critical approaches to research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".