The Internet as a Source of Data to Support the Development of a Quality-of-Life Measure for Eating Disorders
Bibliographic record
Abstract
Despite the attractiveness of the Internet as a data source on individuals' experiences with health conditions, few have studied its use in quality-of-life instrument development. In this article, the authors describe the use of Internet-based unsolicited first-person narratives to supplement qualitative material derived from other sources (published articles and interviews) in the early stages of development of a quality-of-life instrument for eating disorders. In a systematic Internet search, they identified 31 posted first-person narratives. Sixteen (52%) authors had anorexia nervosa, 11 (35%) had bulimia nervosa, and 4 (13%) had either eating disorders not otherwise specified or both diagnoses. Themes arising from the narratives were very similar to those from other sources; however, some specific sensitive topics uniquely expressed in the narratives produced items that the authors later validated in focus groups. Despite some limitations, the Internet was an efficient, inexpensive, and fruitful source of supplementary information for item generation.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".