General practitioner referrals to a child and adolescent mental health service (CAMHS): pre and post COVID-19 pandemic
Bibliographic record
Abstract
Abstract Objectives: To compare the characteristics of GP referrals to CAMHS prior to and over the entire pandemic. Methods: All accepted referrals to a Dublin-based CAMHS between January 1, 2019, and June 30, 2023, were examined. Referral letters were anonymised in batches, and information was extracted directly onto a designated proforma. Results: Before the pandemic (January 2019–February 2020), an average of 17.8 referrals were accepted per month, while during and after the pandemic (March 2020–June 2023), this rose to 18.7 accepted referrals per month. Increases were observed in the clinic’s prioritisation of cases during the pandemic period (54.8% v. 41%, p < .001). Referrals post COVID-19 were older (13.1–13.64 years, p = .010) with a higher proportion of females (50.2% v. 62.1%, p < .001). Internalising disorders increased during the pandemic (68.7% v. 78.7%, p = .001), with self-harm referrals also being notably more frequent (18.5% v. 36.3%, p < .001). Referrals for anxiety (43.0% v. 78.2%, p = .004) and eating disorders (0% v.. 6.2%, p < .001) increased significantly. Referrals for psychosis (8.4% v. 4.8%, p = .032) and autism spectrum disorder (ASD) (26.5% v. 18.7%, p = .008) decreased after the onset of the pandemic. Conclusions: Notable increases in referrals for anxiety, depression, self-harm, and eating disorders underscore the impact of the pandemic on youth mental health. Understanding these shifts is crucial for CAMHS to adapt resources and interventions effectively. Clinicians must remain vigilant in assessing and addressing the evolving mental health needs of youths in the post-COVID era, ensuring timely and appropriate interventions, and resources to mitigate long-term consequences.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".