What is consumer health informatics? A systematic review of published definitions
Bibliographic record
Abstract
BACKGROUND: Consumer health informatics (CHI) is an emerging field that utilizes technology to provide health information to enhance health-care decision making by the public. There is, however, no widely accepted or uniform definition of CHI. A consensus definition would be important for pedagogical reasons, to build capacity and to reduce confusion about what the discipline consists of. AIM: We undertook a systematic review of published definitions of CHI and evaluated them using five quality assessment criteria and measures of similarity. METHODS: Five databases were searched (Embase, Web of Science, MEDLINE, CINAHL and Business Source Complete) resulting in 1101 citations. Twenty-three studies met the inclusion criteria. Definitions were appraised using five criteria (with each scoring out of one): use of published citation, multi-disciplinarity, journal impact, definition comprehensibility, text readability. RESULTS: Most definitions scored low on citation (Mean ± SD: 0.22 ± 0.42), multi-disciplinarity (0.15 ± 0.28) and readability (0.04 ± 0.21) and somewhat higher on IF (0.35 ± 0.45) and definition comprehensibility (idea density) (0.87 ± 0.34) criteria. Overall, the quality of the published definitions was low 1.63 ± 0.80 (out of five). CONCLUSIONS: The definitions of CHI were variable in terms of the quality assessment criteria. This suggests the need for continued discussion amongst consumer health informaticians to develop a clear consensus definition about CHI.
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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 | 0.053 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.038 | 0.030 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".