Evaluation of fear of injections and its association with avoidance of dental treatment
Bibliographic record
Abstract
Aim: The aim of this study was to assess the intensity of needle phobia and to evaluate the different dimensions of fear of dental injections to help provide better care to the patients. Materials and Methods: A simple random technique was used and 250 adult patients above 18 years of age were selected who attended outpatients Department of Operative Dentistry, Baqai Dental College. A modified form of the structured questionnaire used by Milgrom et al. was generated. The items were scored based on the 5-point traditional Likert scale ranging from strongly disagree to strongly agree. Data were analyzed for descriptive analysis (mean, standard deviation) using the software SPSS version 19. Association of gender with fear of dental injections was evaluated using Chi-squared test (P = 0.05). Results: There was a statistically significant difference in fear of dental injections among male and female subjects. The most fearful aspect of dental injection was found to be fear of cross-infection. Fears associated with local anesthesia, for example, inadequate numbness, adverse reaction, and trouble in breathing or swallowing were the least common fears reported by the patients. Conclusion: This study highlighted that understanding the nature and extent of patients' fear of injection is important for dentists to expand their knowledge of the association of fear of dental needles for the impact on the treatment outcome and reluctance of the patients intervene.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".