A Qualitative Review of the Psychometric Properties and Feasibility of Electronic Headache Diaries for Children and Adults: Where We Are and Where We Need to Go
Bibliographic record
Abstract
BACKGROUND: While paper headache pain diaries have been used to determine the effectiveness of headache treatments in clinical trials, recent advances in information and communication technologies have resulted in the burgeoning use of electronic diaries (e-diaries) for headache pain. OBJECTIVE: To qualitatively review headache e-diaries, assess their measurement properties, examine measurement components and compare these components with recommended reporting guidelines. METHODS: The databases Medline, the Cumulative Index to Nursing and Allied Health Literature, Embase, PsychInfo, the Education Resources Information Centre and ISI Web of Science were searched for self-report headache e-diaries for children and adults. A total of 21 publications that involved e-diaries were found; five articles reported on the development of an e-diary and 16 used an e-diary as an outcome measure in randomized controlled trials or observational studies. The diary measures' components, features and psychometric properties, as well as the quality of evidence of their psychometric properties, were evaluated. RESULTS: Five headache e-diaries met the a priori criteria and were included in the final analysis. None of these e-diaries had well-developed evidence of reliability and validity. Three e-diaries showed evidence of feasibility. E-diaries with ad hoc measures developed by the study investigators were most common, with little to no supportive evidence of reliability and⁄or validity. Compliance with the reporting guidelines was variable, with only one-half of the e-diaries measuring the recommended primary outcome of headache frequency. CONCLUSIONS: Specific recommendations regarding the development (including essential components) and testing of headache e-diaries are discussed. Further research is needed to strengthen the measurement of headache pain in clinical trials using headache e-diaries.
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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 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".