Disruptions of sexually transmitted and blood borne infections testing services during the COVID-19 pandemic: accounts of service providers in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Since the onset of the COVID-19 pandemic in March 2020 in Canada, the availability of sexual health services including sexually transmitted and blood-borne infection (STBBI) testing has been negatively impacted in the province of Ontario due to their designation as "non-essential" health services. As a result, many individuals wanting to access sexual healthcare continued to have unmet sexual health needs throughout the pandemic. In response to this, sexual health service providers have adopted alternative models of testing, such as virtual interventions and self-sampling/testing. Our objective was to investigate service providers' experiences of disruptions to STBBI testing during the COVID-19 pandemic in Ontario, Canada, and their acceptability of alternative testing services. METHODS: Between October 2020-February 2021, we conducted semi-structured virtual focus groups (3) and in-depth interviews (11) with a diverse group of sexual health service providers (n = 18) including frontline workers, public health workers, sexual health nurses, physicians, and sexual health educators across Ontario. As part of a larger community-based research study, data collection and analysis were led by three Peer Researchers and a Community Advisory Board was consulted throughout the research process. Transcripts were transcribed verbatim and analysed with NVivo software following grounded theory. RESULTS: Service providers identified the reallocation of public health resources and staff toward COVID-19 management, and closures, reduced hours, and lower in-person capacities at sexual health clinics as the causes for a sharp decline in access to sexual health testing services. Virtual and self-sampling interventions for STBBI testing were adopted to increase service capacity while reducing risks of COVID-19 transmission. Participants suggested that alternative models of testing were more convenient, accessible, safe, comfortable, cost-effective, and less onerous compared to traditional clinic-based models, and that they helped fill the gaps in testing caused by the pandemic. CONCLUSIONS: Acceptability of virtual and self-sampling interventions for STBBI testing was high among service providers, and their lived experiences of implementing such services demonstrated their feasibility in the context of Ontario. There is a need to approach sexual health services as an essential part of healthcare and to sustain sexual health services that meet the needs of diverse individuals.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".