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11 results for “action feedback”
Dataset: Temporal recalibration in response to delayed visual feedback of active versus passive actions
<p>Data set related to the manuscript: </p><p>Kufer, K., Schmitter, C. V, Kircher, T., Straube, B., 2023. Temporal recalibration in response to delayed visual feedback of active versus passive actions: An fMRI study. https://doi.org/10.21203/RS.3.RS-3493865/V1</p><p>Abstract:</p><p>The brain can adapt its expectations about the relative timing of actions and their sensory outcomes in a process known as temporal recalibration. This might occur as the recalibration of timing between the outcome and (1) the motor act (sensorimotor) or (2) tactile/proprioceptive information (inter-sensory). This fMRI recalibration study investigated sensorimotor contributions to temporal recalibration by comparing active and passive conditions. Subjects were repeatedly exposed to delayed (150ms) or undelayed visual stimuli, triggered by active or passive button presses. Recalibration effects were tested in delay detection tasks, including visual and auditory outcomes. We showed that both modalities were affected by visual recalibration. However, an active advantage was observed only in visual conditions. Recalibration was generally associated with the left cerebellum (lobules IV, V and vermis) while action related activation (active > passive) occurred in the right middle/superior frontal gyrus during adaptation and test phases. Recalibration transferred from vision to audition was related to action specic activations in the cingulate cortex, the angular gyrus and left inferior frontal gyrus. Our data provide new insights in sensorimotor contributions to temporal recalibration via the superior frontal gyrus and inter-sensory contributions mediated by the cerebellum.</p>
Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class
<p><strong>This data set includes both qualitative and quantitative data.</strong></p> <p><strong>Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback.</strong></p> <p>1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv</p> <p><br>2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv</p> <p>3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv<br><br><strong>Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades.</strong></p> <p><br>4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv</p> <p>5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv</p> <p><br>6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>
Commonalities and differences in predictive neural processing of discrete vs continuous action feedback
<p>Dataset relative to the following publication:</p> <p>Schmitter, C.V., Steinsträter, O., Kircher, T., van Kemenade, B.M., Straube, B. (2021). Commonalities and differences in predictive neural processing of discrete vs continuous action feedback. <em>NeuroImage.</em> DOI: <a href="https://doi.org/10.1016/j.neuroimage.2021.117745">10.1016/j.neuroimage.2021.117745</a></p> <p> </p> <p>Details can be found in the readme file.</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
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Safety Action Feedback and Engagement (SAFE) Loop
ClinicalTrials.gov study NCT05381441. IPD Sharing: NO. Countries: 1. Publications: 2.
Distinct roles for the cerebellum, angular gyrus and middle temporal gyrus in action-feedback monitoring
<p>Dataset associated with the following publication:</p> <p>van Kemenade, B.M., Arikan, B. E., Podranski, K., Steinsträter, O., Kircher, T., & Straube, B. (2018). Distinct roles for the cerebellum, angular gyrus and middle temporal gyrus in action-feedback monitoring. Cerebral Cortex</p>
Co-Feedback Action of Growth Hormone, PP and PYY on Ghrelin in Bulimia
ClinicalTrials.gov study NCT03338387. IPD Sharing: NO. Countries: 0. Publications: 9.
Effect of Brain Training Through Visual Mirror Feedback, Action Observation Training and Motor Imagery on Orofacial Sensorimotor Variables in Asymptomatic Subjects: A Single-blind Randomized Controlle
ClinicalTrials.gov study NCT04021147. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Steady-State Feedback Actions of Testosterone on Luteinizing Hormone Secretion in Young and Older Men
ClinicalTrials.gov study NCT00431197. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Integration of Augmented Visual Feedback in Action Observation and Motor Imagery Therapy for Parkinson's Disease
ClinicalTrials.gov study NCT07094828. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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