Understanding the Current State of Health Information Exchange in Long-Term Care Homes
Bibliographic record
Abstract
Research Questions: The research questions within this thesis aimed to examine the current state of health information exchange (HIE) processes within the Canadian long-term care (LTC) setting and identify opportunities to improve these processes through the proliferation of health information technology (HIT).\nMethods: The first study undertook a scoping review following Levac et al’s. approach to the methodology. Next, an interpretive study using semi-structured interviews and Hsieh and Shannon's conventional content analysis methodology was undertaken.\nFindings: The scoping review highlighted that effective HIE processes are susceptible to variations in HIT resources, workload, and social and organizational cultures. The findings of the interpretive study describe common breakdowns in HIE processes and identifies opportunities to connect fragmented information flows through HIT proliferation.\nSignificance: We recommend accelerating the implementation and adoption of HIT to facilitate intra- and inter-organizational HIE for direct-care providers, to strengthen the efficiency of HIE processes, and to improve the safety and quality of care within the LTC sector.
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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 | 0.034 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".