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First Author
Hours per Employee
Length of time for a participant to complete the intervention.
D&B Study Quality Rating
D&B Study quality ratings: Rated 1-25 based on 25 questions about study quality: hypothesis, outcomes described in methods, subject characteristics, intervention methods, confounders, clear outcomes, random variability estimates, p values), External Validity (population representativeness in recruitment and participants, intervention in relevant setting), Internal Validity-Bias (subject and study personnel blinding, data dredging, follow-up loss adjustment, appropriateness of statistics, intervention compliance, valid and reliable measures), Internal Validity – Confounding (groups from same populations and recruited over same time period, randomization, group assignment concealed, adjustment for confounders, participant losses taken into account), and Power.
Reviewer Confidence
Raters' confidence (1=Not at all confident, 4=Very confident) that the true intervention effect lies close to the authors' estimate of the effect; this item was derived from the GRADE rating protocol.
Materials Available
'yes' indicates publication author KA estimates that sufficient materials and directions for the intervention were available through the publication or supplementary materials or author-conducted presentations (some estimates concluded after contacting the author) to implement the intervention without support of the article's author.
Prevention Category
1) Primary interventions are designed to prevent illness, injury, and disease before they occur. 2) Secondary interventions, often considered "early interventions," are designed to treat an existing illness or injury, slow its progression, and help the individual recover. 3) Tertiary interventions are designed to help people manage the long-term, and often complex, health problems associated with an injury or illness that has progressed to a point that is debilitating. Some Interventions fit in two categories.
Effect Size
Effects Size groupings (Large, Medium, Small) are listed in the left column and the interventions reporting such effect sizes are presented in the right column. To be reported, the measure must be statistically significant (p< 0.05). All effect sizes, regardless of measure, are reported in terms of small, medium, and large defined by established statistical thresholds (listed in the in review manuscript). Evaluations employing a multi-group design using independent t-tests (a simple between-group comparison) use Cohen's d to measure effect size (small: 0.2, medium: 0.5, large: 0.8; Cohen, 1988). Evaluations investigating the relationship between time (within-person change) and group (e.g., treatment vs control) use the Analysis of Variance (ANOVA), which utilizes eta-squared (ŋ2) or partial eta-squared (partial ŋ2; limiting the scope of the relationship to gain a clearer picture) as the measure of effect (small: 0.01, medium: 0.06, large: < 0.14; Ellis, 2010). Evaluations interested in reporting the weight of a relationship between groups' outcomes or variables may use the correlation statistic R2 to define effect (small: 0.00, medium: 0.03, large: 0.14; J. Cohen, 1988; Ellis, 2010). Finally, those employing Growth-Modeling Analysis (GMA) to assess the difference between the trajectories of the treatment and control groups may use delta (Δ) to measure effect (small: 0.2, medium: 0.5, large: 0.8; Feingold, 2009).