Factors Affecting Managers’ Technology Adoption Decisions in Long-Term Care Homes: A Canadian Exploratory Study Post–COVID-19 Pandemic
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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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".