Can administrative data be used to research health visiting in England? A completeness assessment of the Community Services Dataset
Bibliographic record
Abstract
Introduction: Health visiting is a community service provided to families with children under five in England and is a key focus of early years policy. Individual-level data on health visiting is captured in the Community Services Data Set (CSDS), an administrative dataset of publicly funded community services across England. Analyses of CSDS are considered experimental as the dataset matures. Objectives: In this study, we aimed to identify health visiting contacts in the CSDS and assess the completeness of these data from 2016/17 to 2019/20 compared to external reference data. Methods: We identified the number of the four mandated postnatal health visiting contacts delivered, excluding those scheduled but not attended, between April 2016 and March 2020. We compared counts by local authority (LA) and financial quarter against the Office for Health Improvement and Disparities' Health Visitor Service Delivery Metrics (HVSDM) to identify a subnational subset of complete CSDS data. We explored the representativeness of this subset. Results: During the study period, 10.2 million health visiting contacts were delivered to 2.4 million children in England. Of these, we identified 3.9 million mandated contacts based on CSDS codes and age at time of contact, which represented 44.7% of all mandated contacts reported in the HVSDM for the same period. There were 63 LAs with complete CSDS data in at least one quarter, which were broadly representative of English LAs overall. Variables related to staff characteristics were highly missing and only 13 LAs had four or more successive quarters of complete data needed for longitudinal, child-level analyses. Conclusions: We identified a subnational subset of complete CSDS data, compared to external reference data, which can be used for health visiting research. Until improvements are made to its completeness, analyses (particularly those requiring longitudinal data) may not be generalisable to the whole child population.
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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 | 0.028 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".