Itâs not one size fits all: a case for how equity-based knowledge translation can support rural and remote communities to optimize virtual health care
Bibliographic record
Abstract
CONTEXT: People living in rural and remote British Columbia (BC) in Canada experience complex barriers to care, resulting in poorer health outcomes compared to their urban counterparts. Virtual healthcare (VH) can act as a tool to address some of the care barriers, including reducing travel time, cost, and disruptions to people's lives. Conversely, VH can exacerbate inequities through unique difficulties in rural implementation, such as a lack of access to necessary infrastructure (eg internet), social supports, and technological capacity (eg devices and literacy). ISSUE: The impacts of the COVID-19 pandemic induced a rapid shift to VH, providing new opportunities for health care while simultaneously highlighting and exacerbating inequities for people living in rural and remote settings. Equity-informed knowledge translation processes can help address these concerns. This commentary reports on an equity-informed knowledge translation process engaged by a diverse group of health researchers, community members, and practitioners in BC. LESSONS LEARNED: Informed by equity principles from the Canadian Coalition for Global Health Research, this knowledge exchange and translation process led to the co-creation of two practical tools: a set of VH appointment tip sheets and an open access report. Through stakeholder engagement and literature consultation, VH appointments were found to have many benefits for those in rural and remote communities, including expanding access to basic and specialized health services. However, some hesitation was noted when relying solely on these modes of care, as they can lack relationality, clarity, and time to process medical information. The tip sheets resulting from this process are an interactional-level tool developed to address this concern and optimize VH appointments, for rural patients and care providers. They offer the respective stakeholder group insights on how to actively prepare for and participate in inclusive virtual care. On a systems level, there is a continually echoed need for equity-based processes to ensure that VH is striking the balance of meeting rural health needs without exacerbating inequities. Additionally, incorporating the voices of rural and remote community members is essential. To help address this gap, an open-access report was compiled to serve as a small-scale example of integrating rural voices with existing literature to recommend systems-level adjustments. Overall, VH holds promise as an effective tool for addressing inequities experienced by those living in rural areas. To maximize this potential, rural and remote stakeholders must be proactively engaged and listened to throughout the processes of considering, planning, and implementing shifts in the utilization of VH options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.049 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".