Application of a case-mix classification based on the functional autonomy of the residents for funding long-term care facilities
Bibliographic record
Abstract
INTRODUCTION: increasing public costs for the care of the elderly have created fundamental changes that are redefining the basic principles of health care funding. In the past, overall institutional funding was predominantly tied to spending. In view of the limitations of this approach to funding long-term care facilities, case-mix classification tries to take into account the characteristics of the residents as a tool for predicting costs. Recently, a new case-mix classification based on the functional autonomy profile of the residents - ISO-SMAF profile - was developed in the Province of Quebec, Canada. This classification can be used to change the funding system to base it on the functional autonomy characteristics of the residents. OBJECTIVES: the main objective of this study was to apply the ISO-SMAF classification to funding long-term care facilities in one area of the Province of Quebec and to compare the results of this new funding methodology to the formal methodology. DESIGN: this study used a cross-sectional design. METHODOLOGY: the population under study comprised all residents of all 11 long-term care facilities in the Eastern Townships area of Quebec. Each resident was assessed using the Functional Autonomy Measurement System. The theoretical budget was calculated based on the adjusted cost per year associated with each ISO-SMAF profile derived from a previous economic study. RESULTS: the theoretical budget based on the ISO-SMAF profiles may highlight the under- or over-funding of a facility when compared to the usual funding system based predominantly on the number of beds and hours of care. CONCLUSION: the results of this study show the feasibility of applying the new funding approach to long-term care facilities. However, implementation of the ISO-SMAF classification for funding must be supported by continued and computerised residents' medical files including the Functional Autonomy Measurement System.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".