An International Perspective on Ethical Considerations in Health Economic Evaluation: A Primer for Pediatric Psychologists
Bibliographic record
Abstract
As health care systems grapple with economic pressures, the need to demonstrate the cost-effectiveness of interventions and services in pediatric clinical psychology to inform budget decision-making is becoming more urgent. Ethical considerations in economic evaluation overlap with those of clinical research related to respect for persons, concern for welfare, and justice, and extend beyond these to include those related to the production of economic evidence by clinical psychologist researchers through the conduct of economic evaluation, and to the consumption of this evidence by budget decision-makers. Consideration of ethical issues begins with the process of selecting a technology by researchers for evaluation. Guidelines for the conduct of economic evaluation promote the maximization of benefits in terms of quality-adjusted life years (QALYs) which may introduce inequity in how these benefits are distributed across the population. Equity concerns are addressed in diverse ways across jurisdictions. A health technology assessment framework that considers economic, effectiveness, and safety evidence alongside social, legal, environmental, and ethical concerns has been adopted in public payer systems to widen the scope of information for budget decision-makers. These decision-makers often use value frameworks that regard ethical issues alongside other evidence. As clinical psychologists are increasingly compelled to demonstrate the economic value of new and existing interventions, ethical concerns are becoming more prominent in health system decision-making and pediatric clinical psychologists can play a larger role among decision-making bodies. Knowledge of these issues will prove critical to sound development of behavioral and psychosocial interventions in the U.S. and internationally.
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 | 0.226 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.032 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 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".