The role of gender in the decision to pursue a surgical career: A qualitative, interview-based study

Background: Previous literature has explored the underrepresentation of women in surgery. However, this research has often been quantitative or limited by considering only the perspectives and experiences of women at more advanced career stages. Here, we use a qualitative methodology and a sample of women and men across the career continuum to identify the role that gender plays in the decision to pursue a surgical career. Methods: We audio-recorded and transcribed semi-structured interviews conducted with 12 women and 12 men ranging in their level of medical training from medical students to residents to staff surgeons. We used Braun and Clarke’s six-step approach to thematic analysis to analyze the data, maintaining trustworthiness and credibility by employing strategies including reflexivity and participant input.  Results: Our findings suggested that the characteristics of surgery and early exposure to the profession served as important factors in participants’ decisions to pursue a surgical career. Although not explicitly mentioned by participants, each of these areas may implicitly be gendered. Gender-based factors explicitly mentioned by participants included the surgical lifestyle and experiences with gender discrimination, including sexual harassment. These factors were perceived as challenges that disproportionately affected women and needed to be overcome when pursuing a surgical career. Conclusions: Our findings suggest that gender is more likely to act as a barrier to a career in surgery than as a motivator, especially among women. This suggests a need for early experiences in the operating room and mentorship. Policy change promoting work-life integration and education to target gender discrimination is also recommended

Use of the KT-MCC strategy to improve the quality of decision making for multidisciplinary cancer conferences: a pilot study

Abstract Background Multidisciplinary Cancer Conferences (MCCs) are increasingly used to guide treatment decisions for patients with cancer, though numerous barriers to optimal MCC decision-making quality have been identified. We aimed to improve the quality of MCC decision making through the use of an implementation bundle titled the KT-MCC Strategy. The Strategy included use of discussion tools (standard case intake tool and a synoptic discussion tool), workshops, MCC team and chair training, and audit and feedback. Implementation strategies were selected using a theoretically-rooted and integrated KT approach, meaning members of the target population (MCC participants) assisted with the design and implementation of the intervention and strategies. We evaluated implementation quality of the KT-MCC Strategy and initial signals of impact on decision making quality. Methods This was a before-and-after study design among 4 MCC teams. Baseline data (before-phase) were collected for a period of 2 months to assess the quality of MCC decision making. Study teams selected the intervention strategies they wished to engage with. Post-intervention data (after-phase) were collected for 4 months. Implementation quality outcomes included reach, adherence/fidelity and adaptation. We also evaluated feasibility of data management. Decision making quality was evaluated on a per-case and per-round level using the MTB-MODe and MDT-OARS tools, respectively. Results There were a total of 149 cases and 23 MCCs observed in the before phase and 260 cases and 35 MCCs observed in the after phase. Teams implemented 3/5 strategies; adherence to selected strategies varied by MCC team. The per-round quality of MCCs improved by 11% (41.0 to 47.3, p = < 0.0001). The quality of per-case decision-making did not improve significantly (32.3 to 32.6, p = 0.781). Conclusion While per round MCC decision making quality improved significantly, per-case decision-making quality did not. We posit that the limited improvements on decision making quality may be attributed to implementation quality gaps, including a lack of uptake of and adherence to theoretically-identified implementation strategies. Our findings highlight the importance of evaluating implementation quality and processes, iterative testing, and engagement of key gatekeepers in the implementation process

Use of the theoretical domains framework and behaviour change wheel to develop a novel intervention to improve the quality of multidisciplinary cancer conference decision-making

Abstract Background Multidisciplinary Cancer Conferences (MCCs) are prospective meetings involving cancer specialists to discuss treatment plans for patients with cancer. Despite reported gaps in MCC quality, there have been few efforts to improve its functioning. The purpose of this study was to use theoretically-rooted knowledge translation (KT) theories and frameworks to inform the development of a strategy to improve MCC decision-making quality. Methods A multi-phased approach was used to design an intervention titled the KT-MCC Strategy. First, key informant interviews framed using the Theoretical Domains Framework (TDF) were conducted with MCC participants to identify barriers and facilitators to optimal MCC decision-making. Second, identified TDF domains were mapped to corresponding strategies using the COM-B Behavior Change Wheel to develop the KT-MCC Strategy. Finally, focus groups with MCC participants were held to confirm acceptability of the proposed KT-MCC Strategy. Results Data saturation was reached at n = 21 interviews. Twenty-seven barrier themes and 13 facilitator themes were ascribed to 11 and 10 TDF domains, respectively. Differences in reported barriers by physician specialty were observed. The resulting KT-MCC Strategy included workshops, chair training, team training, standardized intake forms and a synoptic discussion checklist, and, audit and feedback. Focus groups (n = 3, participants 18) confirmed the acceptability of the identified interventions. Conclusion Myriad factors were found to influence MCC decision making. We present a novel application of the TDF and COM-B to the context of MCCs. We comprehensively describe the barriers and facilitators that impact MCC decision making and propose strategies that may positively impact the quality of MCC decision making

Individualized pattern recognition for detecting mind wandering from EEG during live lectures.

Neural correlates of mind wanderingThe ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.Mind wandering detectionTo apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.ConclusionsModelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings