Factors Associated With Dental Service Utilization Based On Andersen Model Among Adults (18 To 64 Years) In PIMS Hospital, Islamabad
Bibliographic record
Abstract
Objectives: To determine the association and frequencies or percentages of factors influencing dental service utilization among adults based on the Andersen model. Methodology: A cross-sectional study with convenience sampling was conducted, involving 385 adults aged 18-64 years. Data on factors based on the Andersen model, including predisposing, enabling, and need-based factors, were collected through a questionnaire. Clinically-assessed need-based factors, such as missing teeth and dental treatment need, were also included. Data were analyzed using SPSS version 26, employing frequencies, percentages, and chi-square tests. Results: Among the study participants, 134 out of 385 individuals (34.8%) had a dental visit in the past year, and 294 out of 385 (76.4%) required dental treatments. Significant associations were observed between dental service utilization and factors such as education, residence, income, self-reported tooth/mouth pain, missing teeth, and treatment need (p < 0.05). These associations were determined while maintaining a 95% confidence interval and a margin of error of 5%. Conclusion: The study found that factors such as education, place of residence, income levels, self-reported mouth pain, missing teeth, and the need for dental treatment significantly influenced dental service utilization among adults. The findings highlight the importance of targeted interventions and policies to improve accessibility and utilization of dental services, particularly for underserved populations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".