Scoping review of adherence promotion theories in pelvic floor muscle training – 2011 ics state‐of‐the‐science seminar research paper i of iv
Bibliographic record
Abstract
AIMS: This paper, the first of four emanating from the International Continence Society's 2011 State-of-the-Science Seminar on pelvic-floor-muscle training (PFMT) adherence, aimed to summarize the literature on theoretical models to promote PFMT adherence, as identified in the research, or suggested by the seminar's expert panel, and recommends future directions for clinical practice and research. METHODS: Existing literature on theories of health behavior were identified through a conventional subject search of electronic databases, reference-list checking, and input from the expert panel. A core eligibility criterion was that the study included a theoretical model to underpin adherence strategies used in an intervention to promote PFM training/exercise. RESULTS: A brief critique of 12 theoretical models/theories is provided and, were appropriate, their use in PFMT adherence strategies identified or examples of possible uses in future studies outlined. CONCLUSION: A better theoretical-based understanding of interventions to promote PFMT adherence through changes in health behaviors is required. The results of this scoping review and expert opinions identified several promising models. Future research should explicitly map the theories behind interventions that are thought to improve adherence in various populations (e.g., perinatal women to prevent or lessen urinary incontinence). In addition, identified behavioral theories applied to PFMT require a process whereby their impact can be evaluated.
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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 | 0.032 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".