A Scoping Review of Tools and Techniques on Evaluating Population Health and Healthy Life Expectancy
Bibliographic record
Abstract
The concept of healthy life expectancy (HLE) integrates the ideas of life expectancy and health status, providing a valuable metric to evaluate both the length and quality of life. This paper seeks to aid policymakers in creating an inclusive HLE indicator system through a systematic review of methodologies for defining and measuring HLE, along with relevant published studies' descriptions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement, two English language literature databases were researched from January 2020 to April 2023. Findings from empirical HLE-related studies were analyzed by extracting data on the study area, design, population, healthy state measurement tools, and results of studies using HLE indicators. The current analysis encompassed 48 empirical studies. Researchers discerned 11 unique HLE indicators within this corpus, each concentrating on a particular aspect. Furthermore, the analysis revealed 18 diverse instruments for evaluating health statuses, each varying in its definition of a healthy state, dimensions of measurement, and the categories of data employed. Therefore, merging global health concepts, HLE indicators, methodologies for assessing healthy states, and applied research demonstrations are essential for a consolidated HLE indicator system creation.
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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 | 0.076 | 0.214 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.055 | 0.050 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".