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Record W2914943293 · doi:10.1111/1440-1630.12570

Understanding quality of life within occupational therapy intervention research: A scoping review

2019· review· en· W2914943293 on OpenAlexaff
Haley Baron, Natalie Hawrylyshyn, Samantha S. Hunt, Janette McDougall

Bibliographic record

VenueAustralian Occupational Therapy Journal · 2019
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsThames Valley Children's CentreWestern University
Fundersnot available
KeywordsOccupational therapyIntervention (counseling)Quality of life (healthcare)Construct (python library)Meaning (existential)PsychologyDescriptive statisticsInclusion (mineral)Quality (philosophy)MedicineNursingPhysical therapyPsychotherapistSocial psychologyComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: There is little consensus regarding the meaning of quality of life (QOL) within occupational therapy literature. Measurement of QOL has varied in both research and practice. This scoping review explored the definitions and measures used within occupational therapy quantitative intervention research to evaluate QOL as an outcome. METHODS: The review was completed by searching six databases using occupational therapy-related and QOL-related terms. Data were extracted from each article and authors performed descriptive statistics to establish trends for both definitions and measures. RESULTS: Eighteen publications met the inclusion criteria. Seven articles included a definition of QOL. Most authors defined QOL as a multidimensional construct, comprised of varying domains. Fourteen different standardised measures and two non-standardised measures were utilised. CONCLUSIONS: A clear conceptualisation of QOL that incorporates occupational therapy values such as client-centredness and holism is needed to advocate for the profession's role in health care and to encourage the development of suitable outcome measures.

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)0.030
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.951
GPT teacher head0.719
Teacher spread0.232 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2019
Admission routes1
Has abstractyes

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