Insights on the Process of Using Interpretive Phenomenological Analysis in a Sport Coaching Research Project
Bibliographic record
Abstract
Interpretative Phenomenological Analysis (IPA) is a qualitative research methodology used to understand participants’ subjective realities through personal interpretations of their lived experiences and the meanings they attach to these experiences (Smith, 2011). IPA has been used predominantly in health psychology, with rising interest within the field of sport psychology and coaching. This article seeks to describe insights about the processes of IPA by a research team using the methodological approach for the first time. These experiences are shared against the backdrop of research exploring the lived experiences of Masters athletes within the context of coached competitive swim programs. We describe how the multiple facets of IPA influence the refinement of the research question, the planning and implementation of data collection, and data analysis and interpretation. We elaborate on our perceptions of the complexities of IPA and make recommendations for how future research teams might smoothly navigate the rigorous research process to yield rich in-depth data and interpretations.
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 | 0.163 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.044 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| 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".