Moving from In-Person to Digital Delivery Models of HIV Peer Support Services in Response to COVID-19: A Qualitative Study
Bibliographic record
Abstract
COVID-19 saw a rapid shift in how community-based peer support programs were delivered. HIV peer support workers were required to work from home and community-based HIV and LGBTQ + organisations moved their support programs to digital platforms. Between May and September 2020, semi-structured interviews were conducted with individuals who worked (n = 17) or volunteered (n = 4) for community-based HIV and/or LGBTQ + health organisations. Interviews explored the impact of moving to digital service delivery on how peer support programs were delivered and the impact on peer support workers. We identified three overarching themes. Firstly, we highlight challenges with rapidly shifting to online service delivery, particularly as this shift limited opportunities for informal interactions between participants. Informal interactions were considered an important aspect of peer support programs. Secondly, the move to digital service delivery diminished opportunities for informal support between peer workers and their colleagues, just as they were also adjusting to a new and unfamiliar working environment. Thirdly, the removing of physical distance as a specific barrier to care opened new opportunities for engaging potential clients who may have previously had difficulties in accessing HIV support services. We argue that careful consideration is needed to address barriers specific to digital service delivery, including lack of access to appropriate technology and telecommunication infrastructure, as well as concerns about participants' privacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".