Bibliographic record
Abstract
Welcome to the second issue of the year of Education for Health (EfH)! As always, we bring to you an assortment of articles that include Original Research, Practical Advice, General Articles, Student Contributions, and Letters to Editors from various countries, encompassing a range of important issues from policy to ground level implementation. The papers provide interesting research insights, share successes, and implementation challenges in healthcare and education of health professionals that eventually inform practice for better health outcomes. Increasing equity in admissions to health professions programs is an international challenge. In their article titled “Preparing for Medical School Selection: Exploring the Complexity of Disadvantage through Applicant Narratives,” Jackson et al. identify the factors that facilitate the application process. Social capital is important and their work identifies how some applicants are using limited resources to accumulate experiences they believe will increase their likelihood of acceptance, despite efforts to create equitable admissions practices. They use a unique lens, bricolage, to discuss how applicants approach these challenges. They encourage changes in admissions practices to determine whether the traits of “bricoleurs” might be desirable for admissions. Research shows that interprofessional education enables collaborative care, with improved patient outcomes. Gilbert et al. in their original research article bring together the experiences from six countries to highlight the need for a global policy to implement interprofessional education for collaborative practice (IPECP). The authors present an interesting synthesis of case studies describing policy initiatives from Canada, Germany, Thailand, Philippines, Uruguay, and India about IPECP implementation and challenges faced. They envisage research-based evidence to inform policy thus facilitating effective implementation of IPECP and eventual inclusion in global accreditation standards. Clithero et al. report on the use of a qualitative analysis process, parallaxis praxis, which integrates arts into the data collection. This methodology was used to explore the students’ understanding of social accountability. Both the topic and the methods used are intriguing, and the study demonstrates how the use of art in data collection might be used to explore how concepts are operationalized for individuals. Exploring meaning in this way encourages interaction and reflection. Exploring the meaning of social accountability in this way allowed participants to develop a deeper meaning of the concept. Chhabra et al. approach social accountability from a different viewpoint. In the brief communication, “Social Responsiveness: The Key Ingredient to Achieve Social Accountability in Education and Health Care,” the role of compassion in providing care and social accountability is discussed. Screening for this characteristic in admissions is one approach. Ensuring that the “hidden curriculum” engenders and emphasizes the importance of compassionate care is another. In another article on expanding opportunities for historically minoritized applicants to medical school, Oguntula et al. report on a program-supporting applicants by preparing them for the computer-based assessment for sampling personal characteristics test. Barriers identified included access to health-care professionals for mentoring and, once again, the pandemic offered a way to increase access. By using an online platform, the innovation allowed collaboration between medical students and medical school applicants. Those with similar lived experiences were able to mentor those applying and support their application process. Reducing financial burdens and building social capital were two important, positive outcomes of this work. Training programs widely employ Kirkpatrick’s model for program evaluation. In their practical advice paper, Sabey et al. describe a modified design in the context of a research methodology training program that includes the individual, organization, and health system. The framework provides a structure encouraging a continuous approach to program evaluation that allows the planning of evaluation activities at the very outset rather than later. It can be adapted to other training programs as well. One letter to the editor in this issue explores the importance of disclosure of medical errors by physicians and its impact on their mental health, advocating support to help them cope, and early learning for students using simulation. In a second letter the author, having held multiple academic roles in his career, shares his “eye opening experience” of completing a medical education fellowship and learning curriculum development formally for the first time. We hope these articles provoke your thinking about health workforce education and motivate you to share your experiences and research through EfH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".