Relative analgesia and general dental practitioners: attitudes and intentions to provide conscious sedation for paediatric dental extractions
Bibliographic record
Abstract
AIM: To examine the attitudes and intentions of general dental practitioners (GDPs) who work within the remit of the National Health Service (NHS) to provide relative analgesia (RA) for paediatric extractions. METHODS: All 45 GDPs working within the boundaries of one Trust were asked to complete a questionnaire to assess demography, etc., intention and attitudes to provide RA for paediatric extractions. RESULTS: Ninety-eight per cent of GDPs took part. All GDPs worked within the NHS. Twenty-nine per cent of GDPs stated that they had RA equipment available in their practices and 68% stated that they discussed RA as treatment alternative. Eighty-seven per cent referred their paediatric extraction cases for dental general anaesthesia. The behavioural intention was predicted by total attitude score and the availability of RA equipment in the practice (R2=0.97, F(37,5)=260.11, P<0.001). Total attitude was predicted by clinical competency, few financial worries or time concerns and the availability of RA equipment (R2=0.91, F(38,4)=106.21, P<0.001). CONCLUSIONS: This study suggests that GDPs' concerns of clinical competence and costs have an inhibiting effect upon their intention to provide RA for paediatric extractions. These concerns must be addressed by planners and policy makers if there is to be a shift from hospital-based DGA to surgery-based RA services for paediatric extractions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".