Potholes in the Information Highway: <i>The Use of Health Service Utilization Data by Alberta Health Care Managers</i>
Bibliographic record
Abstract
Reviews and consultations with regional health authority decision makers have indicated that both data quality and access can limit effective use of health services information when assessing outcomes, planning changes, testing solutions and making decisions. To further a more system-wide understanding of these data utilization issues, we asked senior managers, board members and information analysts in Alberta regional health authorities (n = 111) about the availability of, organizational supports for and barriers to the use of health service utilization data. Eighty percent of respondents stated that the lack of data impeded problem resolution, and 83 percent of managers stated that health service data alerted them to new problems. Examples of useful data related to good standardization and linkage of data sets, or to capacity for valid comparison and trending. Given the limitations highlighted in relation to meeting even the simplest needs of standardization, linkage, comparison and trending, Alberta health care managers indicate frustration in trying to use health service data as currently construed and distributed, particularly within their current frameworks and fast-paced timelines for decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".