Using the English national health service dataset for research into mental health service use among children and young people
Bibliographic record
Abstract
Abstract Background: Routinely collected administrative data, such as the Mental Health Services Data Set (MHSDS) in England, provide opportunities to investigate determinant and patterns of mental health service use across the population. Persistent challenges with data completeness and consistency, however, limit their value for robust population-level research. Objective: To assess the methodological robustness and research utility of the MHSDS for investigating patterns and determinants of mental health service use among children and young people (CYP) in England between 2016 and 2023. Methods: We evaluated the completeness and consistency of key sociodemographic variables (gender, ethnicity, location, and socioeconomic status) over time and across local authorities. To assess the impact of data quality on analytical validity, we modelled the likelihood of care contact attendance using sociodemographic covariates, comparing estimates from complete-case analyses with those from models treating missing or conflicting data as distinct categories. Spatial and temporal patterns of CYP in contact with (referred to) mental health services and attendance rates were examined annually and by local authority. Findings: From 2016 to 2023, over 4.7 million CYP were in contact with mental health services, of whom 62.4% had at least one attended contact. Missing data were substantial, persistent, often co- occurred in subpopulations with distinct attendance patterns. The proportion of CYP referred to services increased from 1.9% (371,655) in 2016 to 9.7% (1,699,899) in 2023, while attendance rates remained stable at 59.8%. Spatial analyses highlighted regional variations, with higher attendance rates in Northern England Conclusions: Data completeness influences the validity of research using the MHSDS. Addressing this issue is essential for producing equitable and reproducible evidence in CYP mental health research. Clinical Implication: Methodological improvements in handling missing data and temporal inconsistencies will strengthen the interpretability, reproducibility, and equity relevance of MHSDS-based mental health research.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".