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Record W2973589927 · doi:10.1515/jcim-2019-0067

“I am a healthcare practitioner”: A qualitative exploration of massage therapists’ professional identity

2019· article· en· W2973589927 on OpenAlexaffabout
Amanda Baskwill, Meredith Vanstone, Del Harnish, Kelly Dore

Bibliographic record

VenueJournal of Complementary and Integrative Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster UniversityImpactHumber Polytechnic
Fundersnot available
KeywordsMassageQualitative researchCompetence (human resources)Health careIdentity (music)MedicineEmpowermentFocus groupAlternative medicineNursingConfusionPsychologySocial psychologySociology

Abstract

fetched live from OpenAlex

BackgroundA division has been described among massage therapists, some who identify as healthcare providers while others identify as service providers. The perceived division creates confusion about what it means to be a massage therapist. ObjectiveThis qualitative study answered, "How do massage therapists in Ontario describe their professional identity?" MethodsQualitative description (QD) was used and data were collected from 33 massage therapists using semi-structured interviews. ResultsThe resulting description of massage therapists' identity in Ontario is the first of its kind. The identity described includes passion as professional motivation in practice, the importance of confidence and competence, a focus on the therapeutic relationship, individualized care, and patient empowerment, and a desire to be recognized for their role within the healthcare system. ConclusionThere is still much to be investigated about massage therapists' identity. Future research will explore whether this description resonates with a larger sample of massage therapists in Ontario.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0200.016
Scholarly communication0.0060.004
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.115
GPT teacher head0.548
Teacher spread0.433 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2019
Admission routes2
Has abstractyes

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