Effective strategies to motivate nursing home residents in oral healthcare and to prevent or reduce responsive behaviours to oral healthcare: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Oral healthcare in nursing homes is less than optimal, with severe consequences for residents' health and quality of life. To provide the best possible oral healthcare to nursing home residents, care providers need strategies that have been proven to be effective. Strategies can either encourage and motivate residents to perform oral healthcare themselves or can prevent or overcome responsive behaviours from residents when care providers assist with oral healthcare. This systematic review aims to identify studies that evaluate the effectiveness of such strategies and to synthesise their evidence. METHODS AND ANALYSIS: We will conduct a comprehensive search in the databases MEDLINE, EMBASE, Evidence Based Reviews--Cochrane Central Register of Controlled Trials, CINAHL and Web of Science for quantitative intervention studies that assess the effectiveness of eligible strategies. 2 reviewers will independently screen titles, abstracts and retrieved full texts for eligibility. In addition, contents of key journals, publications of key authors and reference lists of all studies included will be searched by hand and screened by 2 reviewers. Discrepancies at any stage of the review process will be resolved by consensus. Data extraction will be performed by 1 research team member and checked by a second team member. 2 reviewers will independently assess methodological quality of studies included using 3 validated checklists appropriate for different research designs. We will present a narrative synthesis of study results. ETHICS AND DISSEMINATION: We did not seek ethics approval for this study, as we will not collect primary data and data from studies included cannot be linked to individuals or organisations. We will publish findings of this review in a peer-reviewed paper and present them at an international peer-reviewed conference. TRIAL REGISTRATION NUMBER: CRD42015026439.
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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 | 0.091 | 0.084 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.016 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.012 |
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".