New Data on Access to Mental Health and Addictions Services and Home and Community Care
Bibliographic record
Abstract
As the population ages, more Canadians need home care to help manage their health conditions and live safely at home.For Canadians of all ages, timely access to mental health and addictions services is an area of growing concern.The impact of the COVID-19 pandemic and its strain on health system resources have further highlighted the need to improve services in these areas.The Canadian Institute for Health Information (CIHI) is working with governments across Canada to bridge data gaps, develop indicators and publicly report results as part of a collective effort to improve access in these two sectors.Results for three new indicators were released by CIHI in 2020. Key FindingsOn August 6, 2020, the Ca nadia n Institute for Health Information (CIHI) released results, based on 2018-2019 data, for three new indicators (CIHI 2020):• Self-harm, including suicide.In 2018, 25,000 Canadians were hospitalized or died due to intentional self-harm.• Caregiver distress.Ninety-six percent of individuals receiving long-term home care had an unpaid caregiver.More than one in three of these caregivers were distressed.• New long-term care residents who potentially could have been cared for at home.About one in nine newly admitted longterm care residents potentially could have been cared for at home.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".