The Effect of Verbal Self‐Guidance Training for Overcoming Employment Barriers: A Study of Turkish Women
Bibliographic record
Abstract
Women over the age of 40 were trained in verbal self guidance, a methodology for training people to identify dysfunctional self‐statements and translate them into positive self‐talk. Subsequently, they ( n = 27) had significantly higher self‐efficacy with regard to re‐employment than their counterparts who had been randomly assigned to a control group ( n = 28). In addition, they persisted in job search behavior significantly more so than those in the control group. Job search self‐efficacy completely mediated the effect of the training program on job search behavior. Consequently, they were more likely to find a job in their area of interest within 6 months and 1 year of training than were those women in the control group. Des femmes de plus de 40 ans d’une société musulmane, ont été formées à l’auto‐régulation verbale, une méthode pour former les personnes à identifier les auto‐évaluations dysfonctionnelles et à les traduire en un dialogue intérieur positif. En conséquence, elles ( n = 27) ont une auto‐efficacité significativement plus élevée en ce qui concerne le retour à l’emploi que leurs homologues du groupe contrôle ( n = 28). De plus, elles persistent significativement plus dans le comportement de recherche d’emploi que celles du groupe contrôle. L’auto‐efficacité dans la recherche d’emploi influence complètement l’effet du programme de formation sur le comportement de recherche d’emploi. En conséquence, elles étaient plus susceptibles de trouver un emploi en accord avec leur centre d’intérêts en moins de 6 mois et 1 an de formation que les femmes du groupe contrôle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".