Bibliographic record
Abstract
Background: There is a 40% lifetime prevalence of mental illness in the Western Cape province of South Africa, placing significant pressure on the healthcare system (Herman et al, 2009). Postdischarge continuity of mental healthcare is poor in low-and middle-income settings yet is foundational to preventing relapse, the extent and causes of which are unknown in South Africa. Methods: This mixed methods study examined continuity rates and underlying factors for mental healthcare users discharged from an in-patient district hospital service to primary care in a Cape Town Health sub-district. First, six purposively sampled interviews were conducted with managers and clinicians. Thereafter, retrospective data analysis of 5 818 patients discharged from 01/01/2015 to 31/12/2020 was conducted to determine Continuity, Readmission and Loss to Follow-Up Rates by univariate and bivariate data analysis. Codes and data generated from this were reviewed in a focus group discussion with four primary care Mental Health Nurses. Themes and indicators generated from the different phases were analysed using the Van Olmen Health System Dynamics Framework. Results: Two-thirds of patients (66.6%) had no contact within 30 days of discharge, less than a quarter (24.7%) had attended a clinic visit, and a minority (8.7%) were readmitted. Discontinuity was higher in males, those of working age and in higher income groups. Individual-level barriers to continuity of care included diagnostic complexity, severity and co-morbidity, whilst health system barriers included lack of mental health nurses at certain clinics, cross-district referral complexities, and poor collaboration within facilities and with community-based services, and contextual barriers included violent crime, gangsterism and substance abuse. A paucity of diagnostic coding data and concerns regarding incomplete attendance capturing called into question the validity of the indicators generated. Conclusion: Based on available data, the mental health service in the sub-district under study had poor postdischarge continuity of care, signaling the need for an integrated district mental health services policy, with quality-controlled care continuity indicators. Mixed methods research techniques allowed for the qualitative exploration and explanation of poor continuity. Further research is required which focuses on high-risk groups for poor continuity, and the quality of data collection, analysis and reporting in health districts. Key Words: Primary Mental Healthcare, Continuity of Care, Loss to Follow Up, Readmission, Disengagement from Care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".