Development of the Intersectoral Care Reported by Patients Survey for Primary and Oral Healthcare
Bibliographic record
Abstract
Introduction: Oral and general health are closely related, and many oral and chronic-systemic diseases have the same risk factors. However, in most countries dental and general health care systems are separated. Integration of care for patients with closely related, multiple conditions is therefore important to ensure they receive the best quality of care. We aimed to develop a short and easy-to-understand survey to be used by patients to assess integration of dental and primary health care through their perceptions. Methods: This study used both qualitative and quantitative methods. A modified online Delphi method was used for development of the survey. A panel consisting of seven experts assessed the survey in three rounds. The experts were based in the United States, Germany and the Netherlands and had different professional backgrounds, including general practice and dentistry. The initial framework and set of proposed questions were based on a previous survey with a similar focus. During two discussion rounds and one online round the framework and the questions were assessed and modified by the experts. The survey was then assessed for understandability among 18 patients and thereafter piloted among 199 patients. Results: The final questionnaire derived from the consensus procedure contains thirteen questions which address the following five domains: patients' wishes, expectations, awareness and concerns regarding communication between health care providers; patients' perception of health care providers' knowledge; health care provider-patient communication about health status; utilization of health care; and self-rated health. The consensus procedure also yielded improvements in the understandability of survey items: one survey item was changed from a multiple-choice question into a yes/no question, answer options were added to three other survey items, four survey items were slightly changed in wording, and five items remained unchanged. Conclusion: We developed the short, easy-to-understand Integrated Care Reported by Patients Survey to assess integration of dental and primary health care as perceived by patients. In the future, the developed survey is intended to be tested for validity and translated to other languages. The survey provides opportunities for usage in research and as a tool in quality improvement and feedback systems for health care providers.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".