Randomised clinical trial of a motivational interviewing intervention to improve oral health education amongst older adults in Philadelphia: 12‐month evaluation of non‐clinical outcomes
Bibliographic record
Abstract
OBJECTIVES: We conducted a trial to assess the treatment fidelity of an individual-based oral health education intervention utilising motivational interviewing (MI) techniques and its efficacy when compared to a group-based traditional oral health education intervention (TOHE) and a standard of care group (SC) in a sample from Philadelphia during a 12-month follow-up. BACKGROUND: There is lack of information on how different types of oral health educational interventions affect older adults on non-clinical outcomes including changes in oral health-related quality of life (OHRQoL), oral health self-efficacy (SE) and oral health knowledge (OHK). MATERIALS AND METHODS: One hundred and eighty patients were randomly allocated to TOHE, MI and SC groups. Treatment fidelity was measured in 16 non-study patients. The MI intervention was administered by a public health dental hygienist (PHDH). All interviews were audio-recorded and coded by an expert using the Motivational Interviewing Treatment Integrity (MITI) Code. Multivariable longitudinal regression analyses accounting for baseline demographics and correlated errors due to repeated measures via generalised estimating equation were conducted following an intention to treat approach. RESULTS: Over the 1-year follow-up, SE and OHRQoL scores significantly improved amongst the MI group whereas both outcomes worsened amongst the SC group. During the same period, SE and OHRQoL did not change in the TOHE group. CONCLUSION: Findings from the study support the fidelity of this intervention and the improvement of all non-clinical outcomes after 12 months amongst the MI group.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".