Using a paper-based checklist for promoting increased access to long-acting injectable cabotegravir/rilpivirine among people living with HIV in Toronto: Lessons learned from a quality improvement initiative
Bibliographic record
Abstract
Long-acting injectable (LAI) cabotegravir/rilpivirine (CAB/RPV) offers a promising alternative to daily oral antiretroviral therapy for individuals with HIV, potentially addressing adherence challenges and pill fatigue. However, its implementation may inadvertently widen health disparities. We conducted a qualitative improvement project at a large hospital-based HIV clinic in Toronto to promote equitable access to CAB/RPV LAI. A one-page paper-based checklist was developed and piloted from September 2023 to June 2024 to streamline eligibility screening and guide discussions. Of 232 patients screened, 152 records included the checklist. Among those, 10% were ineligible, 33% eligible, and 5% had uncertain eligibility, with 52% lacking eligibility information. CAB/RPV was discussed in 48% of eligible cases, leading to a 6% initiation rate among those discussions (4/66), plus two additional initiations. This compares to a 2.5% initiation rate among other clinic patients without the intervention. Interviews with clinicians and pharmacists revealed the checklist integrated well, aiding systematic assessment and patient discussions, despite time constraints limiting full documentation. Clinicians cited patient preference for oral pills, needle aversion, and logistical concerns as reasons for declining LAI. Our findings suggest that a structured paper-based tool, while imperfect, can facilitate discussions and improve uptake of LAI HIV treatment, highlighting the potential for electronic medical records to further optimize this process and ensure equitable access.
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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 | 0.032 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".