Personality traits and pre-treatment beliefs and cognitions predicting patient adherence to continuous positive airway pressure: A systematic review
Bibliographic record
Abstract
Adherence to Continuous Positive Airway Pressure (CPAP) for obstructive sleep apnoea (OSA) can be improved by behavioural interventions which modify patients' beliefs and cognitions about OSA, CPAP, and themselves. We have conducted the first systematic review of the literature on beliefs and cognitions held before starting treatment, and personality (which influences the former) that predict the decision to purchase or start CPAP, or CPAP adherence one month or more after CPAP initiation. A systematic search and screen of articles identified 21 eligible publications from an initial 1317. Quality assessment performed using an adapted Newcastle-Ottawa Scale demonstrated that 13 (62%) studies were poor quality and only seven (33%) were high quality. Eighteen factors, such as self-efficacy (confidence) in using CPAP and value placed on health predicted CPAP adherence; however, for only six (33%), utility as an intervention target is known, from calculation of individual predictive power. Studies did not use new behavioural frameworks effective at explaining adherence behaviours, nor did they interview patients to collect in-depth data on barriers and facilitators of CPAP use. Future studies cannot have these limitations if high quality evidence is to be generated for intervention development, which is currently sparse as highlighted by this review.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".