An Investigation of Approaches to Performance Measurement: Applications to Long-term Care in Ontario
Bibliographic record
Abstract
With a growing proportion of older adults in the Canadian population, the sustainability of publicly-funded long-term care (LTC) continues to be a concern. While emphasis has been placed on more efficient care delivery – that is, to increase output with a fixed amount of resources – it is unclear if, and how greater efficiency can be realized in a market where the quantities of inputs and output are carefully regulated by government policies. Furthermore, despite the wealth of information on the association between organizational structure, efficiency, and quality of care from research conducted in the U.S., these relationships have not been extensively studied in Canada. This type of analysis is of particular relevance for Ontario, which has the highest proportion of for-profit, chain-owned nursing homes (also known as ‘LTC homes’) in the country. To explore these questions, econometric methods were applied to evaluative the productivity and technical efficiency of LTC homes in Ontario. The dataset was derived from Statistics Canada’s Residential Care Facilities Survey (RCFS) and consisted of observations from 627 LTC homes collected over 15 years (1996/1997 to 2010/2011). Dynamic panel data models – including random effects, fixed effects, maximum likelihood and quantile regression – were estimated to determine the production function of service providers in this sector. Descriptive results revealed that staffing levels were the highest among municipal LTC homes, followed by not-for-profit and for-profit operators. On average, independent facilities provided more hours of direct care than chain-affiliated LTC homes. Within these facilities, health care aides provided more hours of care than any other category of care personnel; in fact, their contribution to residents’ care increased over time. Results from the dynamic panel data models found chain affiliation, urban location, and the scale of operation to be positive and significant predictors of technical efficiency – controlling for heterogeneity in the residents’ care needs, other organizational attributes (e.g., profit status) and the operating environment (e.g., market concentration). Technical efficiency is one of many aspects of performance. The empirical work presented in this thesis offers examples of methods that can be used to analyze and inform LTC policies in Ontario in the future.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.020 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".