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Record W1726125819 · doi:10.1177/0008417413481956

Psychometric testing of a self-report measure of engagement in productive occupations

2013· article· en· W1726125819 on OpenAlexvenueno aff
Carina Tjörnstrand, Ulrika Bejerholm, Mona Eklund

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsCeiling effectPsychosocialPsychologyInternal consistencyClinical psychologyWork engagementPsychometricsMedicinePsychiatryWork (physics)Alternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational therapists working with clients in productive occupations explicitly or implicitly assess their clients' occupational engagement. PURPOSE: To investigate the psychometric properties of the Profiles of Occupational Engagement in People with Severe Mental Illness: Productive Occupations (POES-P) in terms of internal consistency, initial construct validation, and floor and ceiling effects. METHOD: Participants (n = 93) from six day centres completed the data collection. Correlations between the POES-P and instruments measuring similar and dissimilar attributes, such as satisfaction, psychosocial functioning, and unmet needs, were studied. FINDINGS: A moderate relationship was found between the POES-P and occupational satisfaction (r(s) = 0.43) and a weak one with psychosocial functioning (r(s) = 0.22). The association with researcher-assessed participant engagement was slightly higher (r(s) = 0.37), and the relationship with unmet needs was nonsignificant (r(s) = -0.15). Internal consistency of the POES-P (alpha = 0.85) was good, but the distribution of responses indicated a ceiling effect. IMPLICATIONS: The POES-P seems promising for assessing engagement in work-like occupations but would benefit from further development.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.414
GPT teacher head0.506
Teacher spread0.092 · 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 designObservational
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

Citations19
Published2013
Admission routes1
Has abstractyes

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