The impact of forensic work on home life: the role of emotional labour, segmentation/integration and social support
Bibliographic record
Abstract
Purpose – Adopting a person-environment (P-E) fit approach, the purpose of this paper is to examine the role of emotional labour, segmentation/integration and social support in the development of work-home conflict. Design/methodology/approach – Mental health professionals ( n =118) completed the work-home conflict and home-work conflict scales (Netemeyer et al. , 1996), the segmentation preferences and supplies scales (Kreiner, 2006) and the Mann Emotion Requirements Inventory (Mann, 1999). A social support checklist was also developed to assess the perceived value of work and non-work sources of support. Findings – Contrary to expectation, emotional labour was associated with lower levels of work-home conflict. There was no evidence found for the relevance of a P-E fit approach, rather the results indicated that the perception that the organisation supports the separation of work and home is sufficient in ameliorating work-home conflict. In addition, work-based support was found to reduce work-home conflict. Research limitations/implications – The importance of support within the work environment as a way of reducing work-home conflict has been highlighted. That is, providing a safe environment to discuss anxieties and concerns is a fundamental factor when developing organisational support structure. The importance of providing professionals with choice regarding their preference to segment or integrate work and home has also been highlighted. Based on the contradictory findings with regards to emotional labour and work-home conflict, future research should aim to further examine this relationship within a forensic psychiatric setting. Originality/value – This is the first research paper to explore the role of emotional labour, segmentation/integration and social support in the development of work-home conflict.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".