Does capitation affect the delivery of oral healthcare and access to services? Evidence from a pilot contact in Northern Ireland
Bibliographic record
Abstract
BACKGROUND: In May 2009, the Northern Ireland government introduced General Dental Services (GDS) contracts based on capitation in dental practices newly set up by a corporate dental provider to promote access to dental care in populations that had previously struggled to secure service provision. Dental service provision forms an important component of general health services for the population, but the implications of health system financing on care delivered and the financial cost of services has received relatively little attention in the research literature. The aim of this study is to evaluate the policy effect capitation payment in recently started corporate practices had on the delivery of primary oral healthcare in Northern Ireland and access to services. METHODS: We analysed the policy initiative in Northern Ireland as a natural experiment to find the impact on healthcare delivery of the newly set up corporate practices that use a prospective capitation system to remunerate primary care dentists. Data was collected from GDS claim forms submitted to the Business Services Organisation (BSO) between April 2011 and October 2014. Health and Social Care Board (HSCB) practices operating within a capitation system were matched to a control group, who were remunerated using a retrospective fee-for-service system. RESULTS: No evidence of patient selection was found in the HSCB practices set up by a corporate provider and operated under capitation. However, patients were less likely to visit the dentist and received less treatment when they did attend, compared to those belonging to the control group (P < 0.05). The extent of preventive activity offered and the patient payment charge revenue did not differ between the two practice groups. CONCLUSION: Although remunerating NHS primary care dentists in newly set up corporate practices using a prospective capitation system managed costs within healthcare, there is evidence that this policy may have reduced access to care of registered patients.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".