Improving Family Presence in Long-Term Care during the COVID-19 Pandemic
Bibliographic record
Abstract
Family caregivers play a vital role in supporting the physical and mental health of long-term care (LTC) residents.Due to LTC visitor restrictions during the COVID-19 pandemic, residents (as well as family caregivers) showed significant adverse health outcomes due to a lack of family presence.To respond to these outcomes, eight implementation science teams led research projects in conjunction with Canadian LTC homes to promote the implementation of interventions to improve family presence.Overall, technological and virtual innovations, increased funding to the sector and partnerships with family caregivers were deemed effective methods to promote stronger family presence within LTC.Résumé Les proches aidants jouent un rôle essentiel pour la santé physique et mentale des résidents des établissements de soins de longue durée (SLD).En raison des restrictions imposées aux visiteurs pendant la pandémie de COVID-19, les résidents (ainsi que les proches aidants) ont vécu d'importants effets néfastes sur la santé en raison d'un manque de présence familiale.En réaction à ces résultats, huit équipes en science de la mise en œuvre ont mené des projets de recherche en collaboration avec des établissements de SLD canadiens dans le but de promouvoir la mise en œuvre d'interventions qui visent à favoriser la présence familiale.Dans l'ensemble, les innovations technologiques et virtuelles, l'augmentation du financement du secteur et les partenariats avec les proches aidants sont considérés comme des méthodes efficaces pour favoriser une présence familiale plus soutenue au sein des SLD. Key Takeaways• Partnerships between long-term care (LTC) homes and family caregivers should allow for active engagement in policy development and implementation of programs that improve residents' quality of life.• Technological and virtual innovations are promising avenues for promoting stronger family presence within LTC homes.• Public health policies coupled with under-resourcing in the LTC sector impacts family visitation due to a lack of staffing and infrastructure.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".