Attributes That Influence Human Decision-Making in Complex Health Services: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Humans currently dominate decision-making in both clinical health services and complex health services such as health policy and health regulation. Many assumptions inherent in health service models today are underpinned by Ramsey's Expected Utility Theory, a prominent theory in the field of economics that is rooted in rationality. Rational, evidence-based metrics currently dominate the culture of decision-making in health policy and regulation. However, as the COVID-19 pandemic has shown, rational metrics alone may not suffice in making better policy and regulatory decisions. There are ethical and moral considerations and other complex factors that cannot be reduced to evidence-based rationality alone. Therefore, this scoping review was undertaken to identify and map the attributes that influence human decision-making in complex health services. OBJECTIVE: The objective is to identify and map the attributes that influence human decision-making in complex health services that have been reported in the peer-reviewed literature. METHODS: This scoping review was designed to answer the following research question: what attributes have been reported in the literature that influence human decision-making in complex health services? A clear, reproducible methodology is provided. It is reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards and a recognized framework. As the topic of interest merited broad review to scope and understand literature from a holistic viewpoint, a scoping review of literature was appropriate here. Inclusion and exclusion criteria were developed, and a database search undertaken within 4 search systems-ProQuest, Scopus, PubMed, and Web of Science. RESULTS: The results span 46 years, from 1976 to 2022. A total of 167 papers were identified. After removing duplicates, 81 papers remained. Of these, 77 papers were excluded based on the inclusion and exclusion criteria. The remaining 4 papers were found to be relevant. Citation tracking was undertaken, identifying 4 more relevant papers. Thus, a total of 8 papers were included. These papers were reviewed in detail to identify the human attributes mentioned and count the frequency of mentions. A thematic analysis was conducted to identify the themes. CONCLUSIONS: The results highlight key themes that underline the complex and nuanced nature of human decision-making. The results suggest that rationality is entrenched and may influence the lexicon of our thinking about decision-making. The results also highlight the counter narrative of decision-making underpinned by uniquely human attributes. This may have ramifications for decision-making in complex health services today. The review itself takes a rational approach, and the methods used were suited to this. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/42353.
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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 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.028 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".