Hope-Centred Interventions with Unemployed Clients
Bibliographic record
Abstract
This study investigates the effectiveness of hope-based interventions (Niles, Amundson, & Neault, 2011) used with clients in employment counselling centers who were experiencing low hope. Specifically, five hope-centred interventions were delivered in face-to-face (F2F; n = 27) and online formats (n = 25). All participants completed the Hope-Centred Career Inventory (HCCI; Niles, Yoon, & Amundson, 2011), the General Self-Efficacy Scale (GSE; Schwartzer & Jerusalem, 1995), the Vocational Identity Scale (VIS; Holland, Daiger, & Power, 1980), and the Career Engagement Scale (CES; Hirschi, Freund, & Herrmann, 2014) at the start and at the conclusion of the study. The Enhanced Critical Incident Technique (ECIT; Butterfield, Borgen, Maglio, & Amundson, 2009) was used to identify helpful and hindering factors experienced by the participants as well as factors to consider when delivering the study interventions in the future. Finally, a focus group was used to explore the study participants’ perspectives of the career development counsellors who participated in delivering the study. Results indicate that increasing hope competencies can increase an overall sense of hope and that this increase has a direct and measurable effect on how individuals perceive their career situation. The F2F and online groups experienced similar outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".