Bibliographic record
Abstract
PURPOSE OF REVIEW: Adolescents' dietary intake is an important determinant of health and well-being and is influenced by a complex interaction of environmental, social, psychological, and physiological factors. The complexity of the adolescent diet makes its assessment prone to error, which has prompted researchers and clinicians to turn to technology to reduce this error. Previous reviews have been conducted regarding the use of technology in dietary assessment for adults; however, there are no known reviews for adolescents. Therefore, the purpose of this review is to describe the practical considerations for web-based dietary assessment methods and to evaluate recent evidence on their validity and implications. RECENT FINDINGS: There are numerous web-based dietary assessment methods that are available, valid, and reliable for use in the adolescent population. Web-based methods include both native and web-based applications (or 'apps'), and have been developed for use as food records, 24-h dietary recalls, and food frequency questionnaires. SUMMARY: Web-based methods provide an efficient, cost-effective and practical solution to assess dietary intake; they are less burdensome to respondents and reduce errors and bias. Furthermore, adolescents are technologically savvy and often prefer the use of technology. Web-based methods should be considered when assessing adolescents' dietary intake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".