“I get the referral because nobody else wants to work with this person”: A critical realist analysis of social workers providing care to people living with borderline personality disorder
Bibliographic record
Abstract
Borderline personality disorder (BPD) is a mental health condition characterized by unstable relationships, self-image, and emotions, as well as impulsive behaviors and high rates of morbidity and mortality. Provider-based stigmatization of individuals with BPD is associated with poor health outcomes and increased mortality risk. However, little is known about social workers' experiences in providing care to people living with BPD (PLBPD). This study explored social workers' perspectives on the impact of stigma on care for PLBPD. Between April 2020 and January 2021, qualitative semi-structured interviews were conducted with 41 social workers practicing across the continuum of care (e.g., community services and hospitals) in Ontario, Canada. Data were analyzed using Critical Realist Analysis. Four primary themes emerged: (1) stigmatization of PLBPD due to the complexity of their needs (e.g., avoidance, blame, name-calling by providers); (2) lack of BPD-specific training and education in social work curricula; (3) observed stigmatization of PLBPD by social workers and other professionals (e.g., psychologists and physicians); and (4) suggestions for improving care experiences for PLBPD. Results suggest that provider-based stigma remains a significant quality of care issue affecting PLBPD. Further research is needed to understand the scope of these issues among diverse PLBPD populations, within social work practice, and across interprofessional care settings.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.033 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".