Unraveling How Social Workers Recover from Workplace Bullying Through Rediscovering Self
Bibliographic record
Abstract
Social workers are increasingly sharing stories about witnessing and experiencing the injustice of workplace bullying across varied practice environments. Inherent in many stories are themes of discrimination, trauma, betrayal, anger, shame, and loss. Workplace bullying is essentially an abuse of power within the workplace. In the current workplace bullying literature, there is a disproportionately high representation of quantitative studies that describe and measure the nature, scope and prevalence of workplace bullying and its impact on the physical health, emotional well-being, social relationships, and work performance of targets and bystanders. Although this repository is rich with descriptive knowledge, it lacks the voices and experiences of workplace bullying targets. The purpose of this dissertation study is to examine how social workers recover from WPB. Ecological systems theory and socialist feminist theory provide a theoretical framework to guide this research. By utilizing constructivist grounded theory methodology, 13 registered social workers with active membership with the Alberta College of Social Workers, Canada were interviewed by semi-structured open-ended questions about their experiences of workplace bullying recovery. Four key themes emerged from the data: awareness, responses, impacts and rediscovering self. The findings inform a unique description of workplace bullying recovery and have been adopted into a conceptual framework to illustrate the social processes of workplace bullying recovery. A discussion of key findings of this study along with recommendations for social work educators, professional social work regulatory associations and clinicians working with WPB targets conclude this dissertation.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".