Evaluation of the Jönköping dental fear coping model: a patient perspective
Bibliographic record
Abstract
OBJECTIVE: This study is a part of a project with the aim to construct and evaluate a structured treatment model (the Jönköping Dental Fear Coping Model, DFCM) for the treatment of dental patients. The aim of the present study was to evaluate the DFCM from a patient perspective. MATERIAL AND METHODS: The study was performed at four Public Dental Clinics, with the same 13 dentists and 14 dental hygienists participating in two treatment periods. In Period I, 1351 patients were included and in Period II, 1417. Standard care was used in Period I, and in Period II the professionals had been trained in and worked according to the DFCM. In the evaluation, the outcome measures were self-rated discomfort, pain and tension, and satisfaction with the professionals. RESULTS: In comparison with standard care, less tension was reported among patients treated according to the DFCM, (p = .041), which was also found among female patients in a subgroup analysis (p = .028). Additional subgroup analyses revealed that patients expecting dental treatment (as opposed to examination only) reported less discomfort (p = .033), pain (p = .016) and tension (p = .012) in Period II than in Period I. Patients with low to moderate dental fear reported less pain in Period II than in Period I (p = .014). CONCLUSIONS: The DFCM has several positive effects on adult patients in routine dental care. In a Swedish context, the differences between standard care and treatment according to the model were small but, in part, statistically significant. However, it is important to evaluate the model in further studies to allow generalization to other settings.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".