A critical synthesis of interventions to reduce stigma attached to mental illness
Bibliographic record
Abstract
Background: Interventions have been developed and implemented to reduce the stigma attached to mental illness. However, mental healthcare users are still stigmatised.Objective: The objective of this study was to critically synthesise the best available evidence regarding interventions to reduce stigma attached to mental illness.Method: An exploratory and descriptive research design was followed to identify primary studies; systematic review identifid primary studies answering this research question: What best evidence is available regarding interventions to reduce the stigma attached to mental illness? A search was done on selected electronic databases. Seventeen studies (n = 17) were identifid as providing evidence that answered the research question. The following instruments were used: Critical Appraisal Skills Programme, John Hopkins Nursing Evidence-Based Practice research evidence appraisal tool and the Academy of Nutrition and Dietetics Evidence Analysis Manual. The study was submitted to the Post-graduate Education and Research Committee of the School of Nursing Science at Potchefstroom Campus of North-West University for approval.Results: Results indicated some interventions that reduce the stigma attached to mental illness, such as web-based approaches, printed educational materials, documentary and antistigma fims, as well as live and video performances.Conclusions: Humanising interventions seems to have a positive effect on reducing stigma attached to mental illness. From the results and conclusions recommendations were formulated for nursing practice, nursing education and research.Agtergrond: Ingrypings is ontwikkel en geïmplementeer om die stigma verbonde aan geestesongesteldhede te verminder. Die persone wat aan geestesongesteldhede ly, ondervind egter steeds dat daar 'n stigma aan hulle kleef.Doelstellings: Die doel van die studie was om die beste beskikbare voorbeelde van intervensies om stigmatisering van geestesongesteldhede te verminder, krities saam te vat.Metode: ’n Verkennende en beskrywende navorsingsontwerp is gevolg om primêre studies te identifieer. ’n Sistematiese oorsig is gekies as metode om primêre studies te identifieer om die volgende navorsingsvraag te beantwoord: Wat is die beste beskikbare voorbeelde vaningrypings om die stigma verbonde aan geestesongesteldhede te verminder? ’n Ondersoek is gedoen op ’n uitgesoekte elektroniese databasis.Resultate: Tydens die keuring van studies is 17 studies geïdentifieer (n = 17) as bewyslewering en wat die navorsingsvraag beantwoord. Die volgende instrumente is gebruik: ‘Critical Appraisal Skills Programme’, ‘John Hopkins Nursing Evidence-Based Practice’, ‘Research Evidence Appraisal Tool and Evidence Analysis Manual’, en ‘Academy of Nutrition and Dietetics’.Gevolgtrekking: Die studie is aan die Nagraadse Onderrig- en Navorsingskomitee van die Skool van Verpleegkunde van die Potchefstroomkampus, Noordwes-Universiteit, voorgelê vir goedkeuring. Aanbevelings is geformuleer vir die verpleegpraktyk, verpleegonderrig ennavorsing.
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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 | 0.089 | 0.268 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.035 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".