Assessing Costs and Potential Returns of Evidence-Based Programs for Seniors
Bibliographic record
Abstract
The authors describe the customary tools used by health services researchers to conduct economic evaluations of health interventions. Recognizing the inherent challenges of these tools for utilization in contemporary public health practice, we recommend a practical cost-benefit analysis (PCBA) to allow public health practitioners to assess the economic merits of their existing public health programs. The PCBA estimates what health effects and corresponding medical cost avoidance would be required to support the costs associated with implementing a community-based prevention program. We apply the PCBA to evaluate a statewide evidence-based falls prevention program for seniors in Texas. We estimate a positive return on realized costs due to avoided direct and indirect medical expenses if the program averts 7 falls among 140 participants within the first year. While acknowledging the demonstrated health-related benefits of public health interventions, we provide a practical ex-post economic evaluation methodology to assess return on investment as a more simplistic yet effective alternative for public health practitioners versus contemporary analyses of health services researchers.
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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 | 0.039 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".