Psychosocial and pharmacological interventions for personality disorders in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
Personality disorders (PDs) have a global prevalence of 7.8% and are associated with increased rates of morbidity and mortality. Most research on PDs has been conducted in High Income Countries (HICs). We conducted a systematic review to investigate the effectiveness of psychosocial and pharmacological interventions for personality disorders (PDs) in individuals from Low- and Middle-Income Countries (LMICs.) We systematically searched MEDLINE, Embase, APA PsycInfo, Web of Science, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and The Cochrane Library from inception to January 5, 2023. Inclusion criteria were quantitative studies and grey literature where participants received a psychosocial or pharmacological intervention for PD. Exclusion criteria were qualitative studies, review articles, studies in which PD was not the primary condition, and articles not available in English. The Cochrane Risk of Bias tool version 2.0 and Joanna Briggs Institute instruments were used to measure risk of bias. Studies were pooled by type of study, PD investigated, type of intervention, assessment methods, and outcomes. Sixteen studies met inclusion criteria and were included. Fifteen were intervention studies related to borderline PD. Only one studied mixed PDs. Twelve studies were of psychotherapy, one pharmacotherapy, one combination of both, and two neurostimulation. Most of the studies showed improvement in symptoms though data was largely collected using self-report measures. There were only six RCTs. There is a dearth of literature on interventions for PDs in LMICs and funding bodies should prioritize research in LMICs. Systematic Review Registration Number: PROSPERO CRD42021233415.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".