Clinicians’, patients’ and carers’ perspectives on borderline personality disorder in Pakistan: A mixed methods study protocol
Bibliographic record
Abstract
Borderline Personality Disorder (BPD) is a condition characterised by significant social and occupational impairment and high rates of suicide. In high income countries, mental health professionals carry negative attitudes towards patients with BPD, find it difficult to work with patients with BPD, and even avoid seeing these patients. Negative attitudes and stigma can cause patients to fear mistreatment by health care providers and create additional barriers to care. Patients' self-stigma and illness understanding BPD also affects treatment engagement and outcomes; better knowledge about mental illness predicts intentions to seek care. The perspectives of mental health clinicians and patients on BPD have not been researched in the Pakistani setting and likely differ from other settings due to economic, cultural, and health care system differences. Our study aims to understand the attitudes of mental health clinicians towards patients with BPD in Pakistan using a self-report survey. We also aim to explore explanatory models of illness in individuals with BPD and their family members/carers using a Short Explanatory Model Interview (SEMI). The results of this study are important as we know attitudes and illness understanding greatly impact care. Results of this study will help guide BPD-specific training for mental health clinicians who care for patients with BPD and help inform approaches to interventions for patients with BPD in Pakistan.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".