[Emotion-Focused Therapy-Inspired Intervention to Promote Emotional Regulation and Facilitate Return to Work for Employees on Sick Leave].
Bibliographic record
Abstract
The return to work (RTW) of an employee on sick leave for a common mental disorder can be hampered by various obstacles, including complex or conflictual interpersonal relationships. These dynamics may be accompanied by complex emotions that are poorly regulated or difficult for the employee to identify. Emotion-focused psychotherapy, developed by Leslie Greenberg (2020), offers techniques to promote better emotional regulation, which can support a more harmonious RTW. This article aims to present a set of specific techniques enabling people to deal with the complex and maladaptive emotions that arise in the context of difficult relationships, whether real or anticipated, during RTW. Firstly, the rationale behind working with emotions in psychotherapy and the importance of emotional regulation in a person's functioning will be presented. The techniques of focusing, empty-chair dialogue, and two-chair dialogue will then be explained, together with their specific objectives. These interventions are illustrated using a clinical vignette involving an employee on leave for an adjustment disorder with relationship difficulties with his immediate superior. Although promising, this innovative intervention applied to the specific context of RTW will need to be tested to assess both the employee's emotional functioning and to evaluate the effectiveness of the intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".