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Phenomenology and physiotherapy: meaning in research and practice

2012· article· en· W1575621550 on OpenAlexaff
James Shaw, Denise M. Connelly

Bibliographic record

VenuePhysical Therapy Reviews · 2012
Typearticle
Languageen
FieldPsychology
TopicPhenomenology and Existential Philosophy
Canadian institutionsWestern University
Fundersnot available
KeywordsPhenomenology (philosophy)Interpretative phenomenological analysisConceptualizationRigourMedicinePsychotherapistHealth careQualitative researchPhenomenological methodPhenomenological sociologyEpistemologyNursingPsychologySociologySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.408
GPT teacher head0.536
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2012
Admission routes1
Has abstractyes

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