Trends in long-term care staffing by facility ownership in British Columbia, 1996 to 2006.
Bibliographic record
Abstract
BACKGROUND: Long-term care facilities (nursing homes) in British Columbia consist of a mix of for-profit, not-for-profit non-government, and not-for-profit health-region-owned establishments. This study assesses the extent to which staffing levels have changed by facility ownership category. DATA AND METHODS: With data from Statistics Canada's Residential Care Facilities Survey, various types of care hours per resident-day were examined from 1996 through 2006 for the province of British Columbia. Random effects linear regression modeling was used to investigate the effect of year and ownership on total nursing hours per resident-day, adjusting for resident demographics, case mix, and facility size. RESULTS: From 1996 to 2006, crude mean total nursing hours per resident-day rose from 1.95 to 2.13 hours in for-profit facilities (p = 0.06); from 1.99 to 2.48 hours in not-for-profit non-government facilities (p < 0.001); and from 2.25 to 3.30 hours in not-for-profit health-region-owned facilities (p < 0.001). The adjusted rate of increase in total nursing hours per resident-day was significantly greater in not-for-profit health-region-owned facilities. INTERPRETATION: While total nursing hours per resident-day have increased in all facility groups, the rate of increase was greater in not-for-profit facilities operated by health authorities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".