Factors Affecting Managers’ Technology Adoption Decisions in Long-Term Care Homes: A Canadian Exploratory Study Post–COVID-19 Pandemic
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Bibliographic record
Abstract
Health information technologies (HIT) provide opportunities to support staff as well as residents and their families in long-term care (LTC) homes. Yet, LTC homes lag behind other healthcare organizations in HIT adoption, and little is known about the factors that inform and shape LTC home managers' decisions. We conducted an exploratory Delphi study with a panel of 19 Canadian LTC managers who were surveyed through three iterative rounds (brainstorming, narrowing down, and ranking) to solicit their input on the key factors that influence HIT adoption decisions. An authoritative list of 25 factors, described and ranked in importance, was produced. The top five identified factors were (in order of importance): availability of funding, impact on workload and efficiency, value proposition, ease of use, and impact on residents' outcomes. The findings of this research may inform policies and interventions that provide training and workshop opportunities for managers in LTC and increase the awareness of the advocacy and leadership role that managers can play in advancing technology adoption in support of older adults' care. The results can also be used to support funding from LTC home governing bodies, which is tied to the technology adoption portfolio, to institutionalize the commitment to technological transformation in LTC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it