Rapid implementation mapping to identify implementation determinants and strategies for cervical cancer control in Nigeria

BackgroundCervical cancer constitutes a huge burden among women in Nigeria, particularly HIV-infected women. However, the provision and uptake of cervical cancer screening and treatment is limited in Nigeria. Understanding implementation determinants is essential for the effective translation of such evidence-based interventions into practice, particularly in low-resource settings. COVID-19 pandemic necessitated online collaboration making implementation mapping challenging in some ways, while providing streamlining opportunities. In this study, we describe the use of a virtual online approach for implementation mapping (steps 1–3) to identify implementation determinants, mechanisms, and strategies to implement evidence-based cervical cancer screening and treatment in existing HIV infrastructure in Nigeria.MethodsThis study used a mixed methods study design with a virtual modified nominal group technique (NGT) process aligning with Implementation Mapping steps 1–3. Eleven stakeholders (six program staff and five healthcare providers and administrators) participated in a virtual NGT process which occurred in two phases. The first phase utilized online surveys, and the second phase utilized an NGT and implementation mapping process. The Exploration, Preparation, Implementation and Sustainment (EPIS) framework was used to elicit discussion around determinants and strategies from the outer context (i.e., country and regions), inner organizational context of existing HIV infrastructure, bridging factors that relate to bi-directional influences, and the health innovation to be implemented (in this case cervical cancer screening and treatment). During the NGT, the group ranked implementation barriers and voted on implementation strategies using Mentimeter.ResultsEighteen determinants to integrating cervical cancer screening and treatment into existing comprehensive HIV programs were related to human resources capacity, access to cervical cancer services, logistics management, clinic, and client-related factors. The top 3 determinants included gaps in human resources capacity, poor access to cervical cancer services, and lack of demand for services resulting from lack of awareness about the disease and servicesA set of six core implementation strategies and two enhanced implementation strategies were identified.ConclusionsRapid Implementation Mapping is a feasible and acceptable approach for identifying and articulating implementation determinants, mechanisms, and strategies for complex healthcare interventions in LMICs

Testing Go/No-Go training effects on implicit evaluations of unhealthy and healthy snack foods

OBJECTIVE Despite intending to eat healthy foods, people often yield to temptation. In environments rife with unhealthy food options, a positive implicit evaluation of unhealthy foods may inadvertently influence unhealthy choices. This study investigates if and under which conditions implicit evaluations of unhealthy and healthy foods can be influenced by a computer-based Go/No-Go (GNG) training. DESIGN Undergraduate student participants (N = 161 participants; 117 females, 44 males; M$_{age}$ = 19 years, SD = 2 years) completed a GNG training with two healthy (grape and nut) and two unhealthy (potato chip and cookie) stimuli. Participants were either instructed to inhibit their responses to the potato chip (No-Go Chips/Go Grape) or to a grape (No-Go Grape/Go Chips). MAIN OUTCOME MEASURE Implicit evaluations of chips and grapes were assessed using the Extrinsic Affective Simon Task. RESULTS This GNG training impacted implicit evaluations of chips, but not grapes. GNG training effects were stronger for participants with lower sensitivity for behavioural inhibition measured with the Behavioural Inhibition System scale. CONCLUSION GNG training might help people change implicit food evaluations. More research is needed to understand how individual and training characteristics affect outcomes with the goal of tailoring and optimising the GNG training to produce the strongest effect

Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.

BackgroundAlthough there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited.ObjectiveWe sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general.MethodsWe developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses.ResultsOut of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers' perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks.ConclusionsThese data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in general

Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence–Based Tools in Critical Care: Web-Based Survey Study

BackgroundAlthough there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited. ObjectiveWe sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general. MethodsWe developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses. ResultsOut of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers’ perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks. ConclusionsThese data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in general

Maternal Milk Provision in the Neonatal Intensive Care Unit and Mother&ndash;Infant Emotional Connection for Preterm Infants

Maternal milk (MM) intake during neonatal intensive care unit (NICU) hospitalization is associated with improved neurodevelopment in preterm infants. Underlying mechanisms may include stronger mother&ndash;infant emotional connection. This paper examines associations between MM provision in the NICU with maternal connection to her infant using three factors validated in our sample: maternal sensitivity, emotional concern, and positive interaction/engagement. We studied 70 mothers of infants born &lt;1500 g and/or &lt;32 weeks&rsquo; gestation. Associations between MM provision and mother&ndash;infant connection were modeled using median regression adjusted for clustering. Mothers who provided exclusive MM (i.e., 100% MM, no other milk) reported higher levels of maternal sensitivity by a median score of 2 units (&beta; = 2.00, 95% CI: 0.76, 3.24, p = 0.002) than the mixed group (i.e., MM &lt; 100% days, other milk &ge;1 days), as well as greater emotional concern (&beta; = 3.00, 95% CI: &minus;0.002, 6.00, p = 0.05). Among mothers of very preterm infants, greater milk provision was associated with greater maternal sensitivity, but also with greater emotional concern about meeting the infant&rsquo;s needs. These findings highlight the importance of supporting MM provision and early infant care as an integrated part of lactation support. The findings may also provide insight into links between MM provision in the NICU and infant neurodevelopment

Process evaluation of an academic dissemination and implementation science capacity building program.

The UC San Diego Altman Clinical and Translational Research Institute Dissemination and Implementation Science Center (DISC) launched in 2020 to provide dissemination and implementation science (DIS) training, technical assistance, community engagement, and research advancement. DISC developed a program-wide logic model to inform a process evaluation of member engagement and impact related to DISC services. The DISC Logic Model (DLM) served as the framework for a process evaluation capturing quantitative and qualitative information about scientific activities, outputs, and outcomes. The evaluation involved a multimethod approach with surveys, attendance tracking, feedback forms, documentation of grant outcomes, and promotions metrics (e.g., Twitter engagement). There were 540 DISC Members at the end of year 2 of the DISC. Engagement in the DISC was high with nearly all members endorsing at least one scientific activity. Technical assistance offerings such as DISC Journal Club and consultation were most frequently used. The most common scientific outputs were grant submission (65, 39%), formal mentoring for career award (40, 24%), and paper submission (34, 21%). The DLM facilitated a comprehensive process evaluation of our center. Actionable steps include prioritizing technical assistance, strengthening networking opportunities, identifying streamlined approaches to facilitate DIS grant writing through writing workshops, as well as office hours or organized writing leagues