Bibliographic record
Abstract
This article highlights the results of a qualitative study exploring the perceptions of front-line social workers about the use of therapist self-disclosure (TSD) to work through painful emotional distress subsequent to the disaster when working with clients in the aftermath of a catastrophic event. This study was conducted with focus groups of social workers in health-care and social-service settings in the United States, Canada, and Israel. Transcripts were coded for themes and analyzed using the computer software package Atlas TI. The respondents identified a number of ethical and practical dilemmas. Prominent among them was the use of TSD following a shared traumatic event. In this article, the discussion focuses on circumstances surrounding the use of TSD by practitioners during and immediately following disaster-based emergencies, such as the SARS epidemic in Canada, 9/11, ongoing terrorist acts in Israel, and hurricanes in Florida. This article presents findings specific to TSD. These findings are part of a larger study that examined the impact of man-made and natural disasters on social work practice. The findings reveal a beginning understanding about how and the extent to which TSD is and is not used in the therapeutic relationship when both client and therapist have experienced simultaneous trauma. We also offer a beginning set of recommendations for using TSD with disaster victims.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".