How health managers think about risk and the implications for portfolio theory in health systems
Bibliographic record
Abstract
Attention is now being paid in the health economics literature to the insights offered by portfolio theory. Portfolio theory points to the advantages that arise from consideration of both risk and return when setting priorities. Realising these benefits assumes that managers in the health sector have the same understanding of risk as that suggested by the theory. We set out to explore this issue in interviews with health care managers in Alberta, Canada. To provide a point of reference and possible contrast, we also elicit the views of managers in Alberta's main industry, the oil and gas sector. Interviews were held with 25 managers across the two sectors and thematic analysis applied to draw out the main lessons from the interviews. To the oil and gas managers, risk meant opportunity that was worth taking if the return was high enough. To the health managers, risk was seen mainly in epidemiological terms as hazard and something to be avoided at all costs. Rather than reflecting a different understanding of risk, however, health managers had a more nuanced attitude, which is understandable given the consequences of adverse outcomes and the political furore that then follows. The politicisation of risk in health and its association with adverse outcomes suggests that it might be better to avoid this term when thinking about resource allocation across a portfolio of health promoting interventions and to use uncertainty instead.
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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 | 0.055 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".