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Record W2068685049 · doi:10.1177/030802260506800905

The Artistry of Judgement: A Model for Occupational Therapy Practice

2005· article· en· W2068685049 on OpenAlexaboutno aff
Margo Paterson, Joy Higgs, Susan Wilcox

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsJudgementOccupational therapyConstruct (python library)HumanismPsychologyValue (mathematics)Qualitative researchMedical educationPedagogyEngineering ethicsMedicineSociologyEpistemologySocial sciencePolitical sciencePsychiatryEngineering

Abstract

fetched live from OpenAlex

This paper reports on a model developed through qualitative research to examine the intriguing topic of the artistry of judgement in occupational therapy. The construct of professional practice judgement artistry or PPJA was developed (Paterson and Higgs 2001) to explore the cognitive, metacognitive and humanistic aspects of judgement in professional practice. Fifty-three occupational therapy educators and practitioners from four Commonwealth countries (Australia, Canada, New Zealand and the United Kingdom) participated in focus groups and individual interviews over a one-year period in 2001–2. This research identified a number of dimensions and elements that constitute judgement artistry. The model offers a valuable insight into understanding expertise in professional practice in an era when practitioners are struggling with a demand for increased scientific research knowledge to provide evidence for best practice. This research paper recognises the value of the art of occupational therapy and supports a client-centred approach to practice.

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)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0060.035
Scholarly communication0.0130.012
Open science0.0030.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.292
GPT teacher head0.532
Teacher spread0.240 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations23
Published2005
Admission routes1
Has abstractyes

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