‘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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".