Imagine a mental health service that builds stronger families
Bibliographic record
Abstract
Imagine a mental health service for children with which thousands of families reported high satisfaction (95%), had <10% attrition and showed significant improvement in child outcome for behaviour (n=1062; Externalizing problems, F 6, 1049 = 2851.4; P<0.001; d= 2.4) or anxiety problems (n=235; Internalizing problems, F 6, 228 = 1090.7; P<0.001; d=2.8). Imagine that it had a no-waiting list policy, that it would arrange appointment times around families' schedules (day, evening or night), and that removed barriers to care (no travel, no time off work or school and no stigma). Imagine that this program was based on the best evidence from dozens of studies and systematic reviews of interventions. Imagine that the service was shown to be effective in randomized trials and collected outcome data independently on each family and analyzed these data to improve the program. Imagine that the program customized the care for each family and constantly monitored quality. And imagine that it was cost effective for the health system, with the ability to quickly eliminate waitlists. Strongest Families Institute (SFI) is such a service!
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.026 | 0.025 |
| Insufficient payload (model declined to judge) | 0.076 | 0.018 |
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".