Background/objective: In implementing competence-based medical education (CBME), some Canadian residency programmes recruit clinicians to function as Academic Advisors (AAs). AAs are expected to help monitor residents' progress, coach them longitudinally, and serve as sources of co-regulated learning (Co-RL) to support their developing self-regulated learning (SRL) abilities. Implementing the AA role is optional, meaning each residency programme must decide whether and how to implement it, which could generate uncertainty and heterogeneity in how effectively AAs will "monitor and advise" residents. We sought to clarify how AA-resident dyads collaboratively interpret assessment data from multiple sources, co-create learning goals and action plans and attempt to enhance residents' SRL skills.
Methods: Shortly after each of their six meetings during two years of Internal Medicine residency, we conducted individual, brief interviews with AAs (N = 10) and residents (N = 10). We analysed transcripts using an abductive framework with theory-based and evidence-based sensitizing concepts.
Results: We collected 49 residents and 36 AA 'meeting debriefs', which produced rich data on how dyads variably engaged in SRL and Co-RL. Residents and AAs adopted "learning stances" that oriented their perceptions and approaches to Co-RL. Their stances did not always align within dyads. We found unique patterns in how stances evolved or devolved over time, and in how these changes impacted dyads' Co-RL processes. While some dyads evolved to engage in proactive co-regulation, most stayed consistent or oscillated reactively in their relationships, with little apparent Co-RL focused on helping residents to develop clinical competencies through SRL. We catalogued multiple influential sources of regulation of learning.
Conclusion: The conceptually ideal form of Co-RL was not consistently achieved in this well-intended implementation of AA-resident dyads. To better translate 'coaching over time' from intention to practice, we recommend that residency programmes use Co-RL principles to refine CBME processes, including refining assessment tools, resident orientation sessions and faculty development practices.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/medu.15549 | DOI Listing |
Med Educ
January 2025
Institute of Health Professions Education Assessment and Reform, China Medical University, Shenyang, China.
Front Biosci (Landmark Ed)
November 2024
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, Anhui, China.
Background: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.
View Article and Find Full Text PDFAm J Hum Genet
December 2024
Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institution, Tampa, FL 33612, USA. Electronic address:
Med Educ
October 2024
Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Background/objective: In implementing competence-based medical education (CBME), some Canadian residency programmes recruit clinicians to function as Academic Advisors (AAs). AAs are expected to help monitor residents' progress, coach them longitudinally, and serve as sources of co-regulated learning (Co-RL) to support their developing self-regulated learning (SRL) abilities. Implementing the AA role is optional, meaning each residency programme must decide whether and how to implement it, which could generate uncertainty and heterogeneity in how effectively AAs will "monitor and advise" residents.
View Article and Find Full Text PDFWater Res
November 2024
Department of Civil and Environmental Engineering, University of Macau, Macau SAR, China. Electronic address:
Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!