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Record W4379650319 · doi:10.1080/23303131.2023.2216228

Measuring Oppression Experienced by Service Users While They are Receiving Services from Human Service Organizations

2023· article· en· W4379650319 on OpenAlexafffund
Susan Ramsundarsingh, Micheal L. Shier

Bibliographic record

VenueHuman Services Organizations Management Leadership & Governance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaGovernment of Ontario
KeywordsOppressionService (business)Context (archaeology)Confirmatory factor analysisFocus groupPsychologyKnowledge managementComputer scienceBusinessPolitical scienceMarketingGeography

Abstract

fetched live from OpenAlex

This study uses a sequential mixed-method design to test a measure of service user oppression in human service organization. A five-factor measurement model of service user oppression is tested using confirmatory factor analysis of cross-sectional survey data (n = 374), followed by qualitative focus groups to provide context. All five factors of the Service User Oppression Scale were confirmed indicating that 84.5% of service users had experienced oppression. The research provides a valid and reliable tool to assess service user experiences of oppression.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.007
Science and technology studies0.0040.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.069
GPT teacher head0.283
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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