Phenomenology and physiotherapy: meaning in research and practice
Bibliographic record
Abstract
Background: Phenomenological research has emerged as an important qualitative research methodology in health care, contributing to a comprehensive approach to evidence-informed practice among health-care professionals. Evidence-informed practice in physiotherapy will benefit from integrating phenomenological research in order to attend to the lived-experiences of both clients and therapists involved in physiotherapist–client encounters, building a more sensitive and holistic approach to physiotherapy practice.Objective: This paper explores five major strands of philosophy within the phenomenological tradition and outlines their implications for the study of lived-experiences related to physiotherapy care.Major findings: The phenomenological positions of Edmund Husserl, Martin Heidegger, Maurice Merleau-Ponty, Alfred Schutz, and Max van Manen are discussed. Following a description of each phenomenological view, a brief scenario of how their approach to phenomenological study might be applied to problems in physical therapy practice is addressed. The approaches to phenomenology discussed in the paper are considered in the context of a conceptualization of rigour for phenomenological research.Conclusion: The paper concludes by advocating for multiple approaches to phenomenological research to benefit a diversity of approaches to physiotherapy care.
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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 | 0.051 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".