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Record W2792962658 · doi:10.5014/ajot.2018.026005

Addressing Sex in Occupational Therapy: A Coconstructed Autoethnography

2018· article· en· W2792962658 on OpenAlexaff
Natalie Rose, Claire Hughes

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsYork UniversityToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsOccupational therapyHuman sexualityAutoethnographySex therapyRehabilitationIdentity (music)PsychotherapistPsychologyMedicinePsychiatryPhysical therapyGender studiesSociology

Abstract

fetched live from OpenAlex

Occupational therapy practitioners generally agree that sex is a meaningful occupation and should be part of the rehabilitation process; however, there has been reluctance to include clients' sexual concerns in practice. To explore how occupational therapy is addressing clients' sexual concerns, we (one author with a professional background in occupational therapy and the other who is a consumer of occupational therapy services) used a coconstructed autoethnographic approach that involved shared reflection between us. We discuss insights discovered through the coconstructed process, including the potential health risks involved in sexual activity, the importance of sexuality as a core aspect of identity, the ways that rehabilitation currently excludes sexuality, and the potential role of occupational therapy in addressing sexuality.

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)0.014
metaresearch head score (Gemma)0.016
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 score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.016
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.358
GPT teacher head0.551
Teacher spread0.193 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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