Behavioural determinants of salt consumption among hypertensive individuals
Bibliographic record
Abstract
BACKGROUND: High salt consumption among populations remains a challenge for health professionals dealing with prevention and control of hypertension. The present study aimed to identify the psychosocial predictors of salt consumption among hypertensive individuals, based on an extended version of the Theory of Planned Behaviour (TPB). Three salt consumption behaviours were studied: Behaviour 1- using <4 g of salt per day during cooking; Behaviour 2- avoiding adding salt/table salt use to the prepared foods; and Behaviour 3- avoiding the consumption of foods with high salt content. METHODS: At baseline (n = 108), TPB and additional variables (self-efficacy, habit, past behaviour, hedonic determinant, self-perceived diet quality) were measured. At 2-month follow-up (n = 95), the three behaviours were assessed. Behaviour and intention were sequentially regressed on the study variables, using polytomous logistic regression and hierarchical linear regression with rank transformation, respectively. RESULTS: Behaviour 1 was predicted by intention [odds ratio (OR) = 6.23; 95% confidence interval (CI) = 1.81-21.52], whereas self-efficacy and habit predicted intention. Behaviour 2 exhibited high score mean and low variation and was predicted by self-perceived diet quality (OR = 2.56; 95% CI = 1.03-6.36). Behaviour 3 was predicted by the hedonic determinant (OR = 1.42; 95% CI = 1.01-1.98). CONCLUSIONS: The results indicate that salt-related behaviours are explained by a variety of determinants. Among these determinants, special consideration should be given to motivational and hedonic aspects.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".