Understanding how traumatic re-enactment impacts the workplace: Assisting clients' successful return to work
Bibliographic record
Abstract
BACKGROUND: This research explores the observation that people who have had traumatic life experiences may connect with work in an unhealthy way, impacting their ability to return to work successfully. PURPOSE: This research aims to understand how past traumatic experiences influence career choice and workplace behaviour and, given this understanding, consider how occupational therapists can facilitate change towards a successful return to work. METHODS: Semi-structured interviews were used to gather information from twenty-five clients experiencing depression, anxiety or post-traumatic stress disorder. Clients were followed for six months following their planned return to work date. FINDINGS: Participants identified re-enacting unhealthy past experiences at work through: focusing on the needs of others, seeking acceptance and avoidance. Healthy change was created through engaging in coping strategies and partnering with the occupational therapist and workplace stakeholders on return to work planning. IMPLICATIONS: To facilitate a successful return to work, clients must become aware of how their traumatic histories play a role in their current career choices and workplace behavior. With this awareness, occupational therapists and clients may work together towards creating healthy change in the present.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".