PRELIMINARY REPORT OF UNMET NEEDS IN COMMUNITY-DWELLING OLDER PERSONS IN QUEBEC, CANADA
Bibliographic record
Abstract
In Quebec, Canada, the standardized clinical assessments (quasi-mandatory) of older persons receiving public services are compiled into an integrated software. We conducted a descriptive analysis of disabilities and unmet needs in the population receiving services at home. The population receiving at least one public service at home during 1 year (04/2014-03/2015) was studied. Disabilities were evaluated with the 29-item SMAF covering ADLs (7 items), mobility (6), communication (3), mental functions (5), and IADLs (8). Each item was scored on a 5-level scale from 0 (independent) and 0.5 (with difficulty) to 3 (dependent). For each disability item, available resources to compensate for it were evaluated and a handicap score representing unmet needs was indicated. The percentage of persons having unmet needs by items was examined. A total of 129402 users were assessed during the period (80% of the total population). The non-assessed received very few services. Disabilities (score different than zero) were more frequent in IADL disabilities (60–98% of persons for the 8 IADL), followed by ADL (24–85%) and mental functions (37–66%). The unmet needs were globally more frequent in ADL, communication and mental functions. The highest rates of persons with unmet needs were for the items washing with 11.6%, followed by hearing (10.2%), behavior (10.0%), memory (9.9%), walking inside (9.5%) and judgment (8.7%). While the higher rates of disabilities were IADL as expected, the unmet needs were surprisingly high in items related to ADL and mental functions. Planning of the population future services should address these dimensions in priority.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".