The technical efficiency of oral healthcare provision: Evaluating role substitution in National Health Service dental practices in England
Bibliographic record
Abstract
OBJECTIVES: In many countries increasing use is being made of dental care professionals (DCPs) to provide aspects of clinical activity previously undertaken by dentists. This study evaluates the differences in practice efficiency associated with the utilisation of DCPs in the provision of General Dental Services in the National Health Service (NHS) in England. METHODS: One hundred twenty-one NHS practices completed a questionnaire and shared practice information held at the NHS Business Services Authority. Practice efficiency was estimated using data envelopment analysis with the robustness of the findings checked using Stochastic Frontier Model estimation. RESULTS: Dental practices operated at an estimated mean level of technical efficiency of 64%. Variations among practices in the use of DCPs were not associated with variations in practice efficiency after controlling for other staffing levels, patient population characteristics and practice variables. CONCLUSIONS: The current NHS dental contract limits the potential for efficiency improvements by setting annual practice activity targets that produce little incentive for role substitution. Whilst DCPs may by practising efficiently, this is not reflected in practice-level efficiency, possibly because of dentists using the time released for other non-NHS activity.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".