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Record W4394919910 · doi:10.1080/29949769.2024.2345176

Psychometric evaluation of the Chinese version of the service user psychological empowerment scale in a sample of youth service users

2024· article· en· W4394919910 on OpenAlexaff
Siu‐ming To, Ji‐Kang Chen, Johnson Chun-Sing Cheung, Siu‐Ming Chan, Ming-Wan Yan, Man-yuk Adam Chan, Alex Fong

Bibliographic record

VenueAsia Pacific Journal of Social Work and Development · 2024
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsYork University
Fundersnot available
KeywordsSample (material)Scale (ratio)Service (business)EmpowermentPsychologyApplied psychologyBusinessPolitical scienceMarketingGeography

Abstract

fetched live from OpenAlex

This study aimed to evaluate the psychometric properties of the translated Chinese version of the Service User Psychological Empowerment Scale (C-SUPES) in a youth service setting in Hong Kong. Data were collected from a cross-sectional survey of 381 youth residing in Hong Kong and participating in services provided by community-based children and youth services centres. The results of confirmatory factor analysis indicate a good fit for the proposed threefold factor structure with the original Positive Attitude and Involved Attitude subscales combined. The scale’s criterion validity was supported by significant correlations between creative self-efficacy, youth – adult partnerships, and civic engagement in the community, and its internal consistency reliability was satisfactory. Overall, youth service users with a higher level of participation in youth services reported a higher level of psychological empowerment. Altogether, the results suggest that C-SUPES is a reliable, valid instrument for measuring psychological empowerment among youth service users in Hong Kong.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.075
GPT teacher head0.409
Teacher spread0.335 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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