Who’s in the House? Staffing in Long-Term Care Homes Before and During COVID-19 Pandemic
Bibliographic record
Abstract
Critical gaps exist in our knowledge on how best to provide quality person-centered care to long-term care (LTC) home residents which is closely tied to not knowing what the ideal staff is complement in the home. A survey was created on staffing in LTC homes before and during the COVID-19 pandemic to determine how the staff complement changed. Perspectives were garnered from researchers, clinicians, and policy experts in eight countries and the data provides a first approximation of staffing before and during the pandemic. Five broad categories of staff working in LTC homes were as follows: (1) those responsible for personal and support care, (2) nursing care, (3) medical care, (4) rehabilitation and recreational care, and (5) others. There is limited availability of data related to measuring staff complement in the home and those with similar roles had different titles making it difficult to compare between countries. Nevertheless, the survey results highlight that some categories of staff were either absent or deemed non-essential during the pandemic. We require standardized high-quality workforce data to design better decision-making tools for staffing and planning, which are in line with the complex care needs of the residents and prevent precarious work conditions for staff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".