Patient satisfaction with e-oral health care in rural and remote settings: a systematic review
Bibliographic record
Abstract
BACKGROUND: During the past decade e-oral health technology has been used to address the oral health care challenges in rural and remote settings. This review systematically evaluated the literature on patient satisfaction with e-oral health care in rural and remote communities. METHODS: The systematic review included interventional and observational studies published between 1946 and 2021, in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, and Global Health. Patient satisfaction with received oral health care using self-reported measures, at any time after the intervention, was the main outcome of the review. The other primary outcomes were undesirable consequences of the health care (e-health or conventional) such as diagnostic error. The secondary outcomes considered were waiting time, number of visits, travel, and the cost of oral health care. Two independent researchers assessed the risk of bias using the ROBINS-I risk of bias assessment tool for non-randomized studies. RESULTS: Among 898 studies, 16 studies were included in the review. In most studies reporting patient satisfaction, all patients had shown willingness for teleconsultation for a dental problem and they were mostly satisfied due to saved travel time, saved working days, and prompt treatment onset. Most of the studies acknowledged teledentistry as a cost-effective and cost-saving method. Moreover, the teledentistry consultations showed diagnostic reliability and validity values comparable to conventional dental consultations. The majority of studies were considered level 4 and 3b, due to limited sample populations, analysis based on limited alternatives or costs, non-consistent sensitivity analysis, failure to appropriately control known confounders, and/or failure to carry out an appropriate follow-up of patients. CONCLUSION: Available evidence indicates that e-oral health is associated with higher patient satisfaction and has been found to be an effective and reliable method for patients in rural and remote areas. Therefore, in these areas, the use of e-oral health should be encouraged. However, methodological inconsistencies in the current evidence suggest the need for long-term cohort studies and clinical trials, as well as cost analysis on e-oral health in rural settings. SYSTEMATIC REVIEW REGISTRATION: The systematic review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD42016039942.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".