Maintaining employment and improving health
Bibliographic record
Abstract
Purpose A proportion of the working age population in the UK experience mental health conditions, with this group often facing significant challenges to retain their employment. As part of a broader political commitment to health and well-being at work, the use of job retention services have become part of a suite of interventions designed to support both employers and employees. While rigorous assessment of job retention programmes are lacking, the purpose of this paper is to examine the success of, and distils learning from, a job retention service in England. Design/methodology/approach A qualitative methodology was adopted for this research with semi-structured interviews considered an appropriate method to illuminate key issues. In total, 28 individuals were interviewed, including current and former service users, referrers, employers and job retention staff. Findings Without the support of the job retention service, employees with mental health conditions were reported unlikely to have been maintained their employment status. Additional benefits were also reported, including improved mental health outcomes and impacts on individuals’ personal life. Employers also reported positive benefits in engaging with the job retention service, including feeling better while being able to offer appropriate solutions that were mutually acceptable to the employee and the organisation. Originality/value Job retention programmes are under researched and little is known about their effectiveness and the mechanisms that support individuals at work with mental health conditions. This study adds to the existing evidence and suggests that such interventions are promising in supporting employees and employers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".