Interpretive description as a methodology for sport psychology research
Bibliographic record
Abstract
Qualitative research approaches have become increasingly important in sport psychology. However, descriptive studies (using interviews and content analysis) have dominated the literature. As such, there is a need for greater diversification of approaches to generating knowledge within sport psychology. To address this gap in the literature, the purposes of this study were twofold: (1) present a novel methodological approach that may be useful for advancing qualitative research in sport psychology; and (2) present an exemplar study using the methodology. The exemplar study examined former youth athletes' experiences of sport and the meaning of these experiences in their current adult lives. Thorne's (2008) interpretive description methodology was used. Semi-structured interviews were conducted with 27 former provincial level athletes (M age = 22.05 years). Analysis produced an overarching interpretive finding that participants were overscheduled during adolescence. Despite some negative experiences, participants were able to draw personally meaningful benefits from youth sport that influenced their young adult lives. Athletes also anticipated that their past experiences would influence their behaviors as future guardians of youth sport. The exemplar study demonstrates how interpretive description can be used and adds to the ongoing methodological sophistication that is occurring in the field of sport psychology.
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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 | 0.255 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".