Investigating oral health‐related quality of life in patients with ischaemic stroke in China
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to explore factors associated with oral health-related quality of life (OHRQoL) in patients with ischaemic stroke in China, guided by the theory of planned behaviour (TPB) and Andersen's oral health outcome model. BACKGROUND: Recent studies have reported that the prevalence of stroke is increasing and that stroke patients are facing many oral health related problems. This study provided insights into the role of TPB in the OHRQoL among patients with ischaemic stroke. MATERIALS AND METHODS: Three hundred eight patients with ischaemic stroke from the Department of Neurology of a third-grade class-A hospital in Xuzhou, China, were enrolled in this cross-sectional study between June and September 2020. They completed a questionnaire that included information on socio-demographic characteristics and health behaviours, the Montreal Cognitive Assessment Scale, the Modified Rankin Scale and the Chinese version of the Oral Health Impact Profile-14 scale (OHIP-14). Structural equation modelling (SEM) was used to examine the association among the selected variables based on the TPB and Andersen's oral health outcome model. RESULTS: Among the participants (mean age 65.6), most (57.5%) were male. Their mean OHIP-14 score was 13.3 ± 7.6. Based on the SEM path diagram, behavioural attitude, subjective norms and perceived behavioural control of oral health were positively associated with intention; intention was associated with OHRQoL. The degree of disability was negatively associated with intention. Cognitive function was positively associated with OHRQoL. CONCLUSION: The OHRQoL of hospitalised patients with ischaemic stroke in China is fair to poor and affected by their oral health behaviours. Improved oral health behavioural attitude, subjective norms and perceived behavioural control might help them form good intentions and improve their OHRQoL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".