We need psychologists!
Bibliographic record
Abstract
Paediatricians are more likely than ever to encounter patients with mental health problems on a daily basis. There is a need for investment in proven treatments, such as psychology-based interventions, for children identified with mental health disorders. There are four main arguments supporting the engagement of psychologists for children with mental health problems: there is clear evidence that psychological interventions can effectively treat a wide range of mental health disorders; many parents and children are more open to exploring psychological therapies rather than medication for mental health problems; psychologists are trained and licensed to perform psychoeducational assessments, which can provide invaluable information about a child's learning profile, attention problems and overall intelligence; and behaviour problems in children can be prevented or improved through parent-based interventions. The authors' strongly advocate for the public funding of psychology services - both in collaborative primary care models and in the school setting.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.186 | 0.089 |
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".