Development of a patient-reported outcome measure of digital health literacy for chronic patients: results of a French international online Delphi study
Bibliographic record
Abstract
BACKGROUND: A psychometrically robust patient-reported outcome measure (PROM) to assess digital health literacy for chronic patients is needed in the context of digital health. We defined measurement constructs for a new PROM in previous studies using a systematic review, a qualitative description of constructs from patients, health professionals and an item pool identification process. This study aimed to evaluate the content validity of a digital health literacy PROM for chronic patients using an e-Delphi technique. METHODS: An international three-round online Delphi (e-Delphi) study was conducted among a francophone expert panel gathering academics, clinicians and patient partners. These experts rated the relevance, improvability, and self-ratability of each construct (n = 5) and items (n = 14) of the preliminary version of the PROM on a 5-point Likert scale. Consensus attainment was defined as strong if ≥ 70% panelists agree or strongly agree. A qualitative analysis of comments was carried out to describe personal coping strategies in healthcare expressed by the panel. Qualitative results were presented using a conceptually clustered matrix. RESULTS: Thirty-four experts completed the study (with 10% attrition at the second round and 5% at the third round). The panel included mostly nurses working in clinical practice and academics from nursing science, medicine, public health background and patient partners. Five items were excluded, and one question was added during the consensus attainment process. Qualitative comments describing the panel view of coping strategies in healthcare were analysed. Results showed two important themes that underpin most of personal coping strategies related to using information and communications technologies: 1) questionable patient capacity to assess digital health literacy, 2) digital devices as a factor influencing patient and care. CONCLUSION: Consensus was reached on the relevance, improvability, and self-ratability of 5 constructs and 11 items for a digital health literacy PROM. Evaluation of e-health programs requires validated measurement of digital health literacy including the empowerment construct. This new PROM appears as a relevant tool, but requires further validation.
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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 | 0.089 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".