Gauging the Quality of Qualitative Research in Adapted Physical Activity
Bibliographic record
Abstract
Qualitative inquiry is increasingly being used in adapted physical activity research, which raises questions about how to best evaluate its quality. This article aims to clarify the distinction between quality criteria (the what) and strategies (the how) in qualitative inquiry. An electronic keyword search was used to identify articles pertaining to quality evaluation published between 1995 and 2012 (n=204). A five phase systematic review resulted in the identification of 56 articles for detailed review. Data extraction tables were generated and analyzed for commonalities in terminology and meanings. Six flexible criteria for gauging quality were formulated: reflexivity, credibility, resonance, significant contribution, ethics, and coherence. Strategies for achieving the established criteria were also identified. It is suggested that researchers indicate the paradigm under which they are working and guidelines by which they would like readers to evaluate their work as well as what criteria can be absent without affecting the research value.
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 | 0.726 | 0.809 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".