Phenomenological approaches: challenges and choices
Bibliographic record
Abstract
Phenomenology is a recognised approach for investigating experiences in health research. Difficulties regarding the approach, however, have been documented with even the definitions and terminology sometimes being unclear. In addition to this, there have been claims that many nurse researchers have failed to report how the gap between philosophically related theory and research practice is managed. While legitimacy can be increased by claims regarding theoretical location, there have also been suggestions that engaging too intensely in methodological awareness can hinder the practice and progress of a research project. A balance is therefore required. This article concentrates on the dilemmas and challenges facing a researcher looking for an appropriate method and approach for a study investigating the experiences of stroke survivors. The challenges of using phenomenology as a research method and the approach of interpretative phenomenological analysis are further considered.
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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 | 0.485 | 0.368 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.016 | 0.107 |
| Scholarly communication | 0.035 | 0.057 |
| Open science | 0.013 | 0.029 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".