Attitudes to remote patient monitoring among orthodontists in Ireland - a qualitative study
Bibliographic record
Abstract
Aims: The aim of the study was to explore the attitudes to remote patient monitoring among orthodontists in Ireland. Methods: A descriptive, qualitative study was conducted involving a purposive sample of orthodontists working in private and public orthodontic practices across Ireland. A topic guide was developed. Six focus groups and a single interview involving 16 participants were undertaken. Semi-structured interviews were audio-recorded and transcribed verbatim with Sonix TM software. A thematic analysis approach was carried out for data analysis using MAXQDA TM software. Results and Discussion: Following data analysis, seven main themes were identified. Factors influencing adoption of RPM include patient-driven factors, peer influence, settings/systems, cost-effectiveness, clinical applications, misuse and oversight, attitude for potential adoption of RPM technology. These factors were found to exert important roles as influencers, barriers or both. Orthodontists anticipate broad adoption of RPM technologies in the future allied with an increased array of applications and available technologies. An increased acceptance and penetration of RPM was acknowledged; however, not all non-user participants would be willing to embrace this. Users of RPM technology have a positive attitude towards RPM technology whilst non-users have ambivalent attitudes. Users positively perceive a positive influence of RPM on their practices through increased efficiency, broad usage, financial and time savings. Non-users perceive lack of patient desire, fixed appliances, public orthodontic setting, cost, time, public perception of profession as barriers. Remote monitoring of oral hygiene of patients with fixed appliances may become imbedded into routine care Conclusions: This qualitative study highlights the multifaceted nature of RPM as perceived by the participants with a range of facilitators and barriers identified.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".