Investigating data challenges and service use patterns in mental health care for children and young people in England
Bibliographic record
Abstract
ObjectiveThis study examines challenges in using Mental Health Service Data for research, evaluating data completeness and consistency across key sociodemographic variables. It explores the impact on research reliability, analyses spatial and temporal patterns of service use among children and young people (CYP), and investigates service use by demographics over time. MethodsWe analysed mental health service use among CYP across English local authorities (2016–2023), focusing on data completeness and inconsistencies. We examined records of CYP in contact with mental health services, those who attended care contacts, and those who did not engage after referral. To assess the impact of data quality on research reliability, we modelled appointment attendance likelihood as a function of sociodemographic variables. Additionally, we analysed spatial and temporal trends, stratified by age, gender, ethnicity, and socioeconomic status, to understand variations in service use across regions and over time. ResultsBetween 2016 and 2023, over 4.7 million CYP in England had contact with mental health services, with 62.4% attending at least one care contact. Data completeness varied in time with the increasing health provider submissions, while missing ethnicity, geography, and socioeconomic status persisted, often clustering in subgroups with distinct attendance patterns. Spatial analysis revealed regional variations, with the North East, South East, and South West showing the highest service contact rates, while attendance was highest in Northern England and parts of the Midlands. Contact with mental health services increased from 371,656 CYP in 2016 to 1,699,899 in 2023, though attendance stabilized at 59.8%. Monthly submissions surged towards the end of financial year, highlighting administrative influences. Sociodemographic trends revealed evolving patterns over time. ConclusionThe Mental Health Service Data offers valuable national-level information on CYP using services in England. Our analysis identified data inconsistencies, missingness, and reporting issues. While this data is a useful tool, improved collection practices and methods to address missingness are essential for more reliable insights to inform research and policy.
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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 | 0.032 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".