CIHI Survey: Variations in Canadian Rates of Hospitalization for Ambulatory Care Sensitive Conditions
Bibliographic record
Abstract
M any chronic conditions, such as diabetes or asthma, can be successfully managed in the community.Appropriate screening, ongoing monitoring, prescribing of medications, providing patient education and other supportive measures that help keep these conditions under control are provided in primary healthcare settings.However, sometimes people with such conditions require hospitalization.Although not all admissions for these conditions are avoidable, timely and effective ambulatory care can potentially reduce the risk of hospitalization by possibly preventing or controlling the onset of an illness or condition or by managing the chronic condition (World Health Organization 2005).These conditions are often referred to as ambulatory care sensitive conditions (ACSC).The conditions used to define ACSC in this analysis include angina, asthma, chronic obstructive pulmonary disorder (COPD), diabetes, grand mal status and other epileptic convulsions, heart failure and pulmonary edema and hypertension (Canadian Institute for Health Information 2008).This is based on an adaptation of the widely used definition of ACSC by Billings et al. (1993).Research shows that variations in ACSC hospitalization rates may be related to factors such as differences in access to and quality of primary healthcare (Ansari et al. 2006;Caminal et al. 2004).They may also be due to differences in community-or hospital-based practice patterns or other factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".