Increasing the Offer, Shifting the Offer: Patients' Perspectives on Routinely Offering HIV Counseling and POC Testing in the Health Services Program of an Urban Community Health Centre
Bibliographic record
Abstract
OBJECTIVES Canadian epidemiologic data demonstrate the fallibility of established HIV testing approaches to reach, diagnose, and link to care a significant portion of the population thereby contributing to missed opportunities to reduce onward HIV transmission. Increasing and diversifying entry points to accessing HIV testing may be a successful strategy to reach people who remain undiagnosed. We sought to determine the perspectives of patients on the acceptability of an offer of routine non-targeted provider-initiated HIV counselling and point-of-care (POC) testing in the health services program of a Community Health Centre in downtown Ottawa, the capital of Canada. METHODS Patients aged 18 years and over accessing the Health Services Program for scheduled clinical appointments were approached by research staff with the offer of a POC HIV test with pre- and post-test counselling. All patients accepting the offer and those declining the offer were offered the opportunity to complete an Acceptability Questionnaire. RESULTS Questionnaire responses from eligible patients over four consecutive weeks in 2018 strongly endorse the acceptability of an offer of an HIV test in the context of their scheduled health services appointment for a separate clinical condition. This contention held both for those patients accepting the offer and proceeding to testing and for those patients declining the offer. CONCLUSIONS The perspectives of the patients in our study demonstrate that a routine offer of non-targeted provider-initiated HIV counselling and POC testing was considered not only to be an acceptable, but also an appropriate and welcome intervention in a community health services program. These results suggest the potential for actively engaging more individuals – including those less likely to be engaged through a targeted testing approach – in the documented benefits of the HIV care and treatment cascade by increasing the HIV test offer through routine provider initiation. In addition, at the population level, shifting the offer through venue diversification, similarly shows potential for reducing engagement in ongoing HIV transmission behaviours and practices attributed to those unaware of their HIV positive status. Both outcomes fundamental to the goal of eliminating AIDS by 2030.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".