“You Admit a Resident, You Admit a Family” The Impact of COVID-19 Restrictions on Family Time in Long-Term Care
Bibliographic record
Abstract
Social connection is associated with wellbeing and better health. However, the public health restrictions that were put in place due to COVID-19 disproportionately affected the older adult population, particularly those living in long-term care (LTC). Due to this unprecedent situation, the researchers aimed to understand the perceived impact of pandemic restrictions on families of residents in LTC facilities, and to shed light on how families perceive the strategies put in place helped families stay connected. Reporting on the interview data of a larger mixed-methods study, findings focused on themes of quality of life, quality of care, mental health concerns, communication, and the rules. The rules was an overarching theme and each of the inter-related themes describe the experiences of families feeling dismissed by the health system, stressed about not being able to support their loved one, and helpless during the various lockdowns when staffing was additional strained. These findings highlight how being excluded from decision-making processes, family members and their loved ones were severely impacted by the COVID-19 restrictions and calls for policy changes to be inclusive of families as part of the care team in decision-making for 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".