Implementing Technology Literacy Programs in Retirement Homes and Residential Care Facilities: Conceptual Framework
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic caused widespread societal disruption, with governmental stay-at-home orders resulting in people connecting more via technology rather than in person. This shift had major impacts on older adult residents staying in retirement homes and residential care facilities, where they may lack the technology literacy needed to stay connected. The enTECH Computer Club from the University of Waterloo in Ontario, Canada created a knowledge translation toolkit to support organizations interested in starting technology literacy programs (TLPs) by providing guidance and practical tips. OBJECTIVE: This paper aimed to present a framework for implementing TLPs in retirement homes and residential care facilities through expanding on the knowledge translation toolkit and the framework for person-centered care. METHODS: Major concepts relating to the creation of a TLP in retirement homes and residential care facilities were extracted from the enTECH knowledge translation toolkit. The domains from the framework for person-centered care were modified to fit a TLP context. The concepts identified from the toolkit were sorted into the three framework categories: "structure," "process," and "outcome." Information from the knowledge translation toolkit were extracted into the three categories and synthesized to form foundational principles and potential actions. RESULTS: All 13 domains from the framework for person-centered care were redefined to shift the focus on TLP implementation, with 7 domains under "structure," 4 domains under "process," and 2 domains under "outcome." Domains in the "structure" category focus on developing an organizational infrastructure to deliver a successful TLP; 10 foundational principles and 25 potential actions were identified for this category. Domains in the "process" category focus on outlining procedures taken by stakeholders involved to ensure a smooth transition from conceptualization into action; 12 foundational principles and 9 potential actions were identified for this category. Domains in the "outcome" category focus on evaluating the TLP to consider making any improvements to better serve the needs of older adults and staff; 6 foundational principles and 6 potential actions were identified for this category. CONCLUSIONS: Several domains and their foundational principles and potential actions from the TLP framework were found to be consistent with existing literatures that encourage taking active steps to increase technology literacy in older adults. Although there may be some limitations to the components of the framework with the current state of the pandemic, starting TLPs in the community can yield positive outcomes that will be beneficial to both older adult participants and the organization in the long term.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| 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".