The pre and post-intervention means with their standard deviations (SD) were used, and differences between scores after and before intervention (post-pre differences) were calculated. The SD not reported for these differences were calculated by imputing a correlation coefficient that was calculated in studies with the adequate information, from the pre- and post-SD and the SD of the difference. The weighted mean of these coefficients (r = 0.86) was calculated and applied to the rest of the studies. The effect size was determined using the adjusted Hedges G standardized mean difference (SMD) with a 95% confidence interval. The overall effect size was weighted by the sample size of the studies using the inverse variance method and a random-effects model. The 95% confidence interval (95% CI) and statistical significance were calculated using the z test. The magnitude of the effect was interpreted using Cohen’s criteria: 0.2: small effect; 0.5: medium; and 0.8: large effect. Satisfactory values ≥ 0.6 were considered [13]. The individual and the combined effect of all studies were plotted by forest plot using RevMan software [14], including assessment of risk of bias for individual studies. Due to the small sample size of the studies included in the meta-analysis, separate effects were calculated for each of the VCTA and CPT subscales: omissions, commissions, correct hits, reaction time, and perceptual sensitivity. All of these scales could not be combined into a single global estimate of the intervention effect.

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