Addressing Rural Health Access Inequity by Assessing Potential and Organizational Readiness for Antifragile Electronic Health Project Design in Rural Communities
Bibliographic record
Abstract
There is a rural health access equity gap (especially for specialist care) within Canadian and international universal healthcare systems.Electronic health (eHealth) can address that gap, but pilot projects rarely scale to other contexts, or sustain in their original settings.Antifragile design and complexity informed principles can improve pilot project lifespans.Antifragile entities gain stability from uncertainty, rather than lose integrity.Antifragile operators include optionality, non-linear evaluation, hybrid leadership, starting small, and avoiding suboptimization.The greater the presence of these antifragile indicators, the greater the likelihood a project succeeds in its initial context and scales to others.The antifragile design portfolio (ADP) is comprised an organizational readiness tool and evaluative framework which promotes antifragile operator integration into eHealth implementation in rural communities.Two central findings reflect rural nuance and investigating tacit knowledge of rural implementation facets:1. Output 1: Composite Design Cycle Theory -organizational readiness tool for antifragile eHealth deployment 2. Output 2: Matrix of Scale -evaluative framework to assess antifragility and institutional investment of an eHealth project Together, these outputs can help eHealth programs sustain and scale in rural communities and contribute to addressing the health access equity gap in rural communities and are part of the antifragile design portfolio.Further, these outputs contribute to the creation of placed-based health policy advocated for by the Canadian Medical Association.In a place-based system, strong patient partnership, and care tailored for the needs of the patient can improve outcomes as iii well as health system function.The antifragile design portfolio is uniquely situated as a midlevel theory to inform policymakers and decision makers and help reform rural health policy.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".