Health Provider Experiences in Supporting Social Connectedness Between Families and Older Adults Living in Long-Term Care Homes
Bibliographic record
Abstract
Introduction: Many people, often older adults, living in long-term care homes (OA-LTCH) became socially isolated during the COVID-19 pandemic due to variable restrictions on in-person visits and challenges associated with using technology for social connectivity. Health providers were key to supporting these OA by providing additional care and facilitating their connections with family using technology such as smartphones and iPads. It is important to learn from these experiences to move forwards from the COVID-19 pandemic with evidence-informed strategies that will better position health providers to foster social engagement for OA-LTCH across a range of contextual situations. Objective: This exploratory qualitative description study sought to explore health provider experiences in supporting social connectedness between family members and OA-LTCH within the COVID-19 context. Methods: Qualitative, in-depth semistructured interviews were conducted with 11 health providers. Results: Using inductive qualitative content analysis study findings were represented by the following themes: (a) changes in provider roles and responsibilities while challenging for health providers did not impact their commitment to supporting OA-LTCH social and emotional health, (b) a predominant focus on OA-LTCH physical well-being with resultant neglect for emotional well-being resulted in collective trauma, and (c) health providers faced multiple challenges in using technology to support social connectivity. Conclusion: Study findings suggest the need for increased funding for LTC to support activities and initiatives that promote the well-being of health providers and OA living in LTC, the need to prioritize social well-being during outbreak contexts, and more formalized approaches to guide the appropriate use of technology within LTC.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".