Assessing the impact of oral health disease on quality of life in Ecuador: a mixed-methods study
Bibliographic record
Abstract
Introduction Globally, oral health diseases surpass all other non-communicable diseases in prevalence; however, they are not well studied in underserved regions, where accessibility to dental services and oral health education is disparately worse. In Ecuador, further research is needed to understand such disparities better. We aimed to assess the effect of oral health disease on individuals' quality of life and how social disparities and cultural beliefs shape this. Methods Individuals 18 or older receiving care at mobile or worksite clinics from May to October 2023 were included. A mixed-methods approach was employed, involving semi-structured interviews, Oral Health-Related Quality of Life (OHRQoL) measures, and extra-oral photographs (EOP). Results The sample (n = 528) included mostly females (56.25%) with a mean age of 34.4 ± 9.44. Most participants (88.26%) reported brushing at least twice daily, and less than 5% reported flossing at least once per day. The median OHRQoL score was 4 (min-max), significantly higher among individuals ≥40 years old, holding high school degrees, or not brushing or flossing regularly (p < 0.05). Identified barriers to good oral health included affordability, time, and forgetfulness. Participants not receiving care with a consistent provider reported fear as an additional barrier. Participants receiving worksite dental services reported these barriers to be alleviated. Dental providers were the primary source of oral hygiene education. Most participants reported oral health concerns, most commonly pain, decay, dysphagia, and halitosis - consistent with EOP analysis. Discussion Findings underscore a need for multi-level interventions to advance oral health equity.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".