Workplace cardiovascular risk reduction by healthcare professionals—a systematic review
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease has a significant impact on public health and is largely preventable by addressing modifiable risk factors. As most adults spend on average half of their waking hours at work, this provides a significant opportunity to address modifiable risk factors through health promotion interventions. Healthcare professionals have the knowledge and skills to provide workplace interventions aimed at cardiovascular risk reduction. AIMS: This study was aimed to assess the literature regarding the effect of workplace interventions led by healthcare professionals on cardiovascular risk factors. METHODS: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, MEDLINE, PsycINFO and SPORTDiscus were systematically searched from inception to March 2021. Included studies evaluated impact of workplace interventions by healthcare professionals on cardiovascular health. Data on study design, baseline characteristics, interventions, outcomes and conclusions were extracted and qualitatively analysed. RESULTS: Forty-five studies representing 77 633 participants were included in the analysis. Healthcare professionals involved included: nurses, nurse practitioners, physicians, dietitians, pharmacists, physician assistants, medical technicians/emergency medical technicians and physiotherapists. Workplace interventions by healthcare professionals generally improved surrogate markers of cardiovascular health. Success varied based on provider and nature of the intervention. Addressing motivation and including follow-up were key factors for successful intervention to reduce cardiovascular risk factors. CONCLUSIONS: Workplace health promotion initiatives delivered by healthcare professionals may improve cardiovascular risk markers if they are evidence based and customized for target populations. More research is needed to determine clinical relevance of interventions and ideal interventions for specific employee groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".