Teen Suicide Information on the Internet: A Systematic Analysis of Quality
Bibliographic record
Abstract
OBJECTIVE: To synthesize the literature on youth suicide risk factors (RFs) and prevention strategies (PSs); evaluate quality of information regarding youth suicide RFs and PSs found on selected Canadian websites; determine if website source was related to evidence-based rating (EBR); and determine the association of website quality indicators with EBR. METHODS: Five systematic reviews of youth suicide research were analyzed to assemble the evidence base for RFs and PSs. The top 20 most commonly accessed youth suicide information websites were analyzed for quality indicators and EBR. Univariate logistic regression was conducted to determine if quality indicators predicted statements supported by evidence (SSEs). Multivariate analysis was used to calculate adjusted odds ratios for SSEs and quality indicators. RESULTS: Only 44.2% of statements were SSEs. The 10 most highly ranked websites contained almost 80% of the total statements analyzed, and one-half had a negative EBR. Compared with government websites, nonprofit organization websites were more likely (OR 1.45, 95% CI 0.66 to 3.18), and personal and media websites were less likely (OR 0.62, 95% CI 0.26 to 1.47), to have a positive EBR. Crediting of an author (AOR 2.65, 95% CI 1.34 to 5.28), and recommendation to consult a health professional (AOR 2.08, 95% CI 1.18 to 3.68), increased the odds of SSEs. CONCLUSIONS: Fundamental to addressing youth suicide is the availability of high-quality, evidence-based information accessible to the public, health providers, and policy-makers. Many websites, including those sponsored by the federal government and national organizations, need to improve the evidence-based quality of the information provided.
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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 | 0.052 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".