Outcomes-based health human resource planning for maternal, child and youth health care in Canada: A new horizon for the 21st century
Bibliographic record
Abstract
Ideally, health human resource (HHR) planning for maternal, child and youth health care should be based not only on an understanding of present and future health care needs, but also on well-defined health-related outcomes. Most of the previous HHR strategies have relied on predictive mathematical models based on the number of existing health care professionals (most often physician numbers) and changes in both total population and population demographics. However, alterations in demands related to health status (or ‘need’) or desired population healthrelated goals (or ‘outcomes’) were not specifically or strategically addressed. Given the unprecedented demand for resources from the impending ‘silver tsunami’ of aging baby boomers, ensuring that pregnant women, children and youth compete favourably for health care resources is vital. The present article offers examples of relatively accessible data sources that are available at a population level and suitable for assessing maternal and child health care needs, details the limitations of a simple needs-based approach, and describes a more comprehensive and relevant outcomes-based HHR planning horizon suitable for the 21st century. We also highlight the importance of innovative models of care that service an effective, sustainable and high-quality health care system. Finally, we argue that this new outcomesoriented, interprofessional framework will be the most effective strategy for improving maternal, child and youth health in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".