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Record W4415778933 · doi:10.1111/jar.70147

‘I Don't Think They've Ever Seen People Like Me Do Jobs Like This’: Exploring Hope Within Strengths‐Based Employment Services for People With Disability

2025· article· en· W4415778933 on OpenAlexafffund
Kelly Carr‐Kirby, Sean Horton, Chad A. Sutherland, Victoria Paraschak, Patricia L. Weir

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of CanadaAdministration for Community LivingOntario Trillium Foundation
KeywordsSupported employmentIntellectual disabilityDisabled peopleDisability discriminationInclusion (mineral)

Abstract

fetched live from OpenAlex

BACKGROUND: Strengths-based employment services focused on the abilities of people with intellectual disabilities challenge traditional, deficits-based orientations. Within the presence of hope, which sustains collective effort toward a preferred future, such employment services may stimulate social change. Therefore, the presence of hope was examined within strengths-based employment settings for adults with intellectual disabilities to understand its potential to establish inclusive workplaces. METHODS: Employees with intellectual disabilities supported by strength-based employment services, as well as their employers and co-workers, completed semi-structured interviews. Data were analysed using thematic analysis. RESULTS: Hope was present within strength-based employment services through the (a) co-sharing of strengths, (b) movement toward a shared, preferred future, (c) alignment of personal and collective goals, and (d) (co)transformation of interacting parties. CONCLUSION: Implementing strengths-based employment services for people with intellectual disabilities is supported, as it facilitates the cultivation of hope and thus movement toward inclusive and equitable workplaces.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.075
GPT teacher head0.376
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes2
Has abstractyes

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