Managers’ and Administrators’ Perspectives on Digital Technology Use in Regional Long-Term Care Homes During the COVID-19 Pandemic
Bibliographic record
Abstract
In this paper, we explore managers' and administrators' perspectives on digital technology use for residents during province-wide lockdowns (June-August 2021) during the COVID-19 pandemic in seven regional long-term care homes (LTC) in Niagara, Canada. Fifteen semi-structured interviews were conducted with participants representing operational, financial, and recreational departments where we discussed their needs and factors influencing the use of digital technology during the phases of increased restrictions on visitors and social isolation. Our findings indicate extensive use of cellular devices including smartphones, however additional iPads were needed to meet the ever-rising demand for virtual connections. Almost all participants revealed supportive leadership, redeployed staff, and community donations as main facilitators for technology use. Barriers related to managing varying elderly cognitive capacities and technical issues affected technology use. Based on our findings, we conclude that financial commitment and community support are integral for future-proofing LTC homes with technological innovations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".