Creating a new normal: an exploration of the protective factors that allow social workers to remain in the profession despite challenges and stress
Bibliographic record
Abstract
Most people will be exposed to at least one traumatic event in their lifetime (Kilian, 2016). The profession of social work is inundated with ongoing exposure to trauma, traumatic stories and disclosures because social workers are called on often to support people experiencing alarming and catastrophic events. Due to the cumulative exposures to traumas or stories of trauma that social workers hear in the workplace, they are at increased risk for lingering negative impacts (Bell, 1995; Courtois, 2002; Stamm, 2002). This study was based on qualitative interviews with five social workers who have been providing direct client service for a minimum of five years. Participants also had not taken stress leave as the intention was to explore some of the protective factors that assist social workers navigating challenges and stressors. The participants used pseudonyms to share their person experiences of resilience, self-care, burnout, values, balance, stigma and shame and the impacts of toxic workplaces. Understanding some of the impacts and the protective factors social workers experience may assist us in the development and implementation best practices for social worker sustainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".