What a Pandemic Has Taught Us About the Potential for Innovation in Rural Health: Commencing an Ethnography in Canada, the United States, Sweden, and Australia
Bibliographic record
Abstract
The COVID-19 pandemic coincided with a multi-national federally funded research project examining the potential for health and care services in small rural areas to identify and implement innovations in service delivery. The project has a strong focus on electronic health (eHealth) but covers other areas of innovation as well. The project has been designed as an ethnography to prelude a realist evaluation, asking the question under what conditions can local health and care services take responsibility for designing and implementing new service models that meet local needs? The project had already engaged with several health care practitioners and research students based in Canada, Sweden, Australia, and the United States. Our attention is particularly on rural communities with fewer than 5,000 residents and which are relatively isolated from larger service centres. Between March and September 2020, the project team undertook ethnographic and auto-ethnographic research in their own communities to investigate what the service model responses to the pandemic were, and the extent to which local service managers were able to customize their responses to suit the needs of their communities. An initial program theory drawn from the extant literature suggested that “successful” response to the pandemic would depend on a level of local autonomy, “absorptive capacity, * ” strong service-community connections, an “anti-fragile † ” approach to implementing change, and a realistic recognition of the historical barriers to implementing eHealth and other innovations in these types of rural communities. The field research in 2020 has refined the theory by focusing even more attention on absorptive capacity and community connections, and by suggesting that some level of ignorance of the barriers to innovation may be beneficial. The research also emphasized the role and power of external actors to the community which had not been well-explored in the literature. This paper will summarize both what the field research revealed about the capacity to respond well to the COVID-19 challenge and highlight the gaps in innovative strategies at a managerial level required for rapid response to system stress. * Absorptive Capacity is defined as the ability of an organization (community, clinic, hospital) to adapt to change. Organizations with flexible capacity can incorporate change in a productive fashion, while those with rigid capacity take longer to adapt, and may do so inappropriately. † Antifragility is defined as an entities' ability to gain stability through stress. Biological examples include building muscle through consistent use, and bones becoming stronger through subtle stress. Antifragility has been used as a guiding principle in programme implementation in the past.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".