Bibliographic record
Abstract
BACKGROUND: Unmet home care needs have been linked to poor health, increased use of other health services, admission to nursing homes and reduced emotional well-being. DATA AND METHODS: Using data from the 2015/2016 Canadian Community Health Survey, this article describes home care use and unmet home care needs by type (i.e., home health care [HHC] and support services) in community-dwelling adults. Among the population with home care needs, the degree to which needs were met, partially met or unmet is presented, as well as information about the barriers to obtaining home care services and the places services were sought. Multivariate analysis was used to examine factors associated with unmet home care needs by type, while controlling for predisposing, enabling and needs-related factors. RESULTS: In 2015/2016, just over one-third (35.4%) of people with home care needs, an estimated 433,000 people, did not have those needs met. This was more prevalent among those with support needs than those with HHC needs. Availability of services was most often cited as a barrier to obtaining home care services, particularly for those with an unmet need for HHC services. Age group, household type, long-term care insurance and health status factors were associated with perceiving an unmet home care need, with few differences by type of unmet need. DISCUSSION: The degree to which needs were met and the perceived barriers to obtaining home care services varied by type of unmet home care need. The results indicate proportionally higher unmet needs for home care services among adults aged 35 to 49. This suggests a possible service gap.
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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 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".