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Record W2061399858 · doi:10.1002/chp.20020

Multisource feedback systems for quality improvement in the health professions: Assessing occupational therapists in practice

2009· article· en· W2061399858 on OpenAlexaff
Claudio Violato, Leanne Worsfold, Jan Miller Polgar

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern UniversityOntario Society of Occupational TherapistsUniversity of Calgary
Fundersnot available
KeywordsCronbach's alphaReliability (semiconductor)Scale (ratio)Variance (accounting)Sample (material)Health professionalsPsychologyApplied psychologyQuality (philosophy)MedicineMedical educationClinical psychologyNursingPsychometricsHealth care

Abstract

fetched live from OpenAlex

INTRODUCTION: The objective was to develop and psychometrically evaluate (feasibility, reliability, validity) a questionnaire-based multisource feedback (MSF) system for quality improvement (QI) for occupational therapists (OTs). METHODS: Surveys were developed for assessment of OTs by clients, co-workers, and themselves, respectively, using 5-point scales with an "unable to assess" category. A sample of 238 OTs participated. RESULTS: The number of respondents for the co-worker questionnaire was 2621, and for the client questionnaire it was 2881. The mean ratings ranged from 4 to 5 for each item on each scale. All of the instruments' full scales had very high Cronbach's alpha > 0.92. The factor analyses revealed a 7-factor solution (66.3% of the total variance) for the co-worker survey, and a 4-factor solution for the client questionnaire (73.2% of the variance). DISCUSSION: An MSF system employing surveys that have high reliability, validity, and feasibility was developed to provide feedback to OTs on core competencies and skills. It is suggested that similar MSF systems are feasible for health professionals in general.

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)0.047
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.166
GPT teacher head0.596
Teacher spread0.430 · 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 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

Citations11
Published2009
Admission routes1
Has abstractyes

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