A qualitative study exploring strategies to improve the inter-professional management of diabetes and periodontitis
Bibliographic record
Abstract
AIMS: To explore inter-professional communication and collaboration in guideline-concordant diabetes and periodontitis care. METHODS: Qualitative design using iterations of workshops to identify ways to improve multidisciplinary working attended by staff from medical and dental primary care practices, and people with diabetes (n=43). Workshops were semi-structured around a topic guide. Recruitment was via the UK Clinical Research Network, and a patient and public involvement group in the North of England. RESULTS: Medical practice participants were unaware of the bidirectional evidence linking diabetes and periodontitis and stated that they had never received a referral from a dental professional in this context. The patient participants with diabetes reported never having been informed about the links between diabetes and periodontitis from either their family physician or dentist. Medical and dental practice participants gave negative accounts of inter-professional communication, with claims of inappropriate requests and defensive or non-responses that stymied future interaction. Indirect communication through the patient was suggested as an alternative to direct communication. CONCLUSIONS: Indirect referral, whereby the patient is signposted to a healthcare professional, was suggested by medical and dental professionals as a useful alternative to the traditional (and time consuming) letter or telephone call, particularly in the case of suspected diabetes or periodontitis.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".