Stakeholder Readiness for Telehomecare: Implications for Implementation
Bibliographic record
Abstract
Numerous pilot studies have demonstrated that telehomecare technology may improve client outcomes through timely intervention and health crises prevention, thereby reducing return visits to hospitals and physician offices. Although the potential of telehomecare to increase access to services and improve quality of care and health outcomes is recognized, expectations for its widespread adoption have not been realized. Factors affecting diffusion of innovations include, among other things, perceptions of the technology, organizational characteristics, and communication. These require further exploration for telehealth applications because evidence alone will not automatically produce large-scale conversions in practice. This 12-month study was designed to assess the readiness of clients, health care professionals, and organizations to adopt telehomecare services for adult diabetic clients within the Calgary Health Region. A qualitative approach was used to collect data through focus groups with clients and home care nurses along with interviews with family physicians and key informants responsible for planning and resource allocation in diabetic homecare and telehealth programs. The transcripts of these interviews were analyzed for themes, which were categorized with respect to their effect on quality of care (including structure, process or outcome of care), including those related to the individual client, the health care provider, and the organization as a whole. The study findings identified differences in stakeholder conceptions of the technology, including common themes among clients, providers, and organizations. Implications of study results for developing a strategy to incorporate telehomecare into routine community care are discussed.
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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 | 0.065 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".