An evaluation of the PALS after treatment modelling intervention to reduce dental anxiety in child dental patients
Bibliographic record
Abstract
AIM: The aim of this study was to assess the effectiveness of the passivity to activity through live symbolic (PALS) after treatment modelling intervention to reduce child dental anxiety. METHODS: A convenience sample of consecutive 5- to 10-year-old dental patients were randomly assigned to intervention or control groups. Self-reported child dental anxiety was assessed at the start of each visit. At the end of each visit, children in the intervention group were introduced to a glove puppet, which acted as the PALS model. The intervention group children re-enacted the treatment they had just received on the puppet's teeth. At the end of each visit, the control children received motivational rewards only. The change in dental anxiety scores was examined by t-tests and analysis of covariance. RESULTS: The final analysis included 27 intervention children and 26 control children. For the intervention group, there were no statistically significant changes in dental anxiety over a course of treatment, between first and second preventive visits, between first and second invasive treatment visits, or between first attendance and subsequent recall attendance. For the control group, a statistically significant decrease in dental anxiety was observed between the first and second invasive dental treatment visits. CONCLUSION: The PALS after treatment modelling intervention was ineffective in reducing child dental anxiety.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".