Health Professionals’ Insights into the Impacts of Privately-Funded Care within a National Health Service: A Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The UK's publicly provided National Health Service (NHS) is primarily publicly funded but treats some private-pay patients (PPPs). Little is known about impacts of treating PPPs within publicly provided health systems. This study explores NHS health professionals' experiences and understanding of this phenomenon. METHODS: Semi-structured interviews were carried out with NHS clinicians. The interview transcripts were then thematically analyzed. RESULTS: A total of 17 clinicians highlighted potential impacts in five areas: (1) availability of resources for non-urgent, publicly funded patients, (2) patient safety for publicly funded patients and PPPs, (3) health professional training, (4) NHS finances, and (5) NHS direction setting and values. CONCLUSIONS: In a publicly provided health service that is increasingly treating PPPs, clinicians had limited knowledge of policies for PPP care. Clinicians were concerned about patient safety impacts of prioritizing PPPs over publicly funded patients. Potential cross-subsidies from public to private funding were mooted. The issues raised here require further exploration and may inform research and policy development in the UK and other countries.
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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 | 0.030 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".