Nurses’ clinical practices reducing the impact of HIV-related stigmatisation in non-HIV-specialised healthcare settings: a protocol for a realist synthesis
Bibliographic record
Abstract
INTRODUCTION: Despite tremendous progress in care, people living with HIV (PLHIV) continue to experience HIV-related stigmatisation by nurses in non-HIV-specialised healthcare settings. This has consequences for the health of PLHIV and the spread of the virus. In the province of Quebec (Canada), only four interventions aimed at reducing the impact of HIV-related stigmatisation by nurses have been implemented since the beginning of the HIV pandemic. While mentoring and persuasion could be promising strategies, expression of fears of HIV could have deleterious effects on nurses' attitudes towards PLHIV. In literature reviews on stigma reduction interventions, the contextual elements in which these interventions have been implemented is not considered. In order to develop new interventions, we need to understand how the mechanisms (M) by which interventions (I) interact with contexts (C) produce their outcomes (O). METHODS AND ANALYSIS: Realist synthesis (RS) was selected to formulate a programme theory that will rely on CIMO configuration to describe (1) nursing practices that may influence stigmatisation experiences by PLHIV in non-HIV-specialised healthcare settings, and (2) interventions that may promote the adoption of such practices by nurses. The RS will draw on the steps recommended by Pawson: clarify the scope of the review; search for evidence; appraise primary studies and extract data; synthesise evidence and draw conclusions. To allow an acute interpretation of the disparities between HIV-related stigmatisation experiences depending on people's serological status, an initial version of the programme theory will be formulated from data gathered from scientific and grey literature, and then consolidated through realist interviews with various stakeholders (PLHIV, nurses, community workers and researchers). ETHICS AND DISSEMINATION: Ethical approval for realist interviews will be sought following the initial programme theory design. We intend to share the final programme theory with intervention developers via scientific publications and recommendations to community organisations that counter HIV-related stigmatisation.
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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 | 0.136 | 0.149 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
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".