Benefits of a supportive development climate for older workers
Bibliographic record
Abstract
Purpose This study aims to test a model of the relationships among older workers' propensity to engage in development activities (development orientation), their perceptions of the development opportunities associated with their job (job development climate), their commitment to their organization, and their intention to remain with their organization. Design/methodology/approach Separate questionnaires were completed by 395 individuals aged 50 to 70, who were in their career job and 195 individuals aged 50 to 70 who were employed in a bridge job. Both questionnaires included measures of development orientation, job development climate, affective commitment and intention to remain as well as individual characteristics and organizational characteristics. Findings The findings supported the proposed model in that development orientation was positively related to job development climate which, in turn, was positively related to affective commitment and affective commitment was positively related to intention to remain with the organization. There were both similarities and differences in the patterns of relationships for career‐job and bridge‐job respondents. Research limitations/implications The question of causality cannot be determined because of the cross‐sectional research design. Practical implications To create a supportive development climate and retain older workers, employers need to foster older workers' development orientation and ensure that their work assignments provide opportunities to learn new knowledge and skills. Orginality/value There is little empirical research addressing issues related to the development and retention of older workers. No previous studies have investigated both development orientation and job development climate in the context of older workers.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".