Telemedicine perceptions and experiences of socially vulnerable households during the early stages of the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Early in the COVID-19 pandemic, efforts to decrease risk of viral transmission triggered an abrupt shift from ambulatory health care delivery toward telemedicine. In this study, we explore the perceptions and experiences of telemedicine among socially vulnerable households and suggest strategies to increase equity in telemedicine access. METHODS: Conducted between August 2020 and February 2021, this exploratory qualitative study involved in-depth interviews with members of socially vulnerable households needing health care. Participants were recruited from a food bank and primary care practice in Montréal. Digitally recorded telephone interviews focused on experiences and perceptions related to telemedicine access and use. In our thematic analysis, we employed the framework method to facilitate comparison, and the identification of patterns and themes. RESULTS: Twenty-nine participants were interviewed, 48% of whom presented as women. Almost all sought health care in the early stages of the pandemic, 69% of which was received via telemedicine. Four themes emerged from the analysis: delays in seeking health care owing to competing priorities and perceptions that COVID-19-related health care took precedence; challenges with appointment booking and logistics given complex online systems, administrative inefficiencies, long wait times and missed calls; issues around quality and continuity of care; and conditional acceptance of telemedicine for certain health problems, and in exceptional circumstances. INTERPRETATION: Early in the pandemic, participants report telemedicine delivery did not accommodate the diverse needs and capacities of socially vulnerable populations. Patient education, logistical support and care delivery by a trusted provider are suggested solutions, in addition to policies supporting digital equity and quality standards to promote telemedicine access and appropriate use.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".