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A peer‐reviewed collection of reports on innovative approaches to medical education

2012· article· en· W2332502925 on OpenAlexaboutno aff
M. Brownell Anderson

Bibliographic record

VenueMedical Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationCurriculumFormative assessmentObjective structured clinical examinationMedicineMedical ethicsPsychologyPedagogy

Abstract

fetched live from OpenAlex

Critical reflection: lessons learned from a communication skills assessment Using Google Docs to enhance medical student reflection Using hospital art in medical student reflection Students accompany acute hospital admissions from primary care ‘The Game’: student ‘teaching’ objective structured clinical examinations in South Africa Web-based blog supplement to evidence-based physical examination teaching Communication gaps in a teaching paediatric out-patient scenario ‘Care Factor’: engaging medical students with their well-being A novel student-selected component in medical admissions Empowering students to become involved in medical education Framework for feedback: the peer mini-clinical examination as a formative assessment tool Medical students teach basic life support in hospital Course evaluation respondents: are ‘low-performing retaliators’ really over-represented? Da Vinci’s notebook: a novel learning tool Oriented paediatric resuscitation: a new training approach Providing support to preceptors with resident doctors in difficulty Themed monthly evaluations: a focus on individual competencies E-learning strategies to improve general practitioners’ knowledge of age-related macular degeneration The ambulatory morbidity and mortality conference meets the morning report Evaluation of electronic versus traditional format poster presentations A Parisian-style salon addressing social determinants of health An innovative medical Spanish curriculum for resident doctors Broadening horizons: looking beyond disability ‘Imitating Art’: ethics, humanities and professionalism in undergraduate medical education Enhancing medical professionalism through interactive seminars Medical professionalism adapted to faith and cultural beliefs Addressing complex multi-dimensional health problems using interprofessional education Teaching ethics: are students getting the answers? The ethics script concordance test in assessing ethical reasoning ‘Really Good Stuff’ has been part of Medical Education since 2001 and the section has evolved in the years since then to better meet the needs of its readers. The field of medical education itself has also evolved and ‘Really Good Stuff’ serves as a harbinger of many of the new and innovative approaches emerging within it. However, it is challenging to explain a new concept that is no more than 3 years old, in around 500 words, and it is particularly challenging to provide any type of measurement of the impact of the innovation that is useful. It became clear to many of us involved in ‘Really Good Stuff’ that asking authors to write about the ‘results’ of their good stuff left reviewers confused about what the results meant, provided little useful information for anyone attempting to put into place the really good thing described in the report, and presented a rather artificial set of information about the new idea or concept. A change to the format of ‘Really Good Stuff’ was necessary in order to highlight the key points presented in the reports. After much discussion with the editor and the members of the journal’s international editorial board, we changed the format to organise the report content around the three questions: What problems were addressed? What was tried? What lessons were learned? This is the first time the reports have been presented in this new format and thus this collection represents another evolution of the section. All of the reports submitted incorporated the new format well. The external reviews focused on suggestions for changes to the content or issues of clarity, although a very few reviewers still sought evaluative data and ‘results’ from the reports. Fourteen countries are represented in this issue. Of the 29 reports published, four were written collaboratively by authors from two different countries. Perspectives on the topics of medical professionalism, working with other health professions (interprofessionalism), reflection on the part of students, and teaching as well as understanding ethics in different cultures are presented from Egypt, Saudi Arabia, the USA, Taiwan, Australia and Brazil. Different approaches to education and assessment are presented from South Africa, Canada, Spain and India. I hope that one of the lessons you learn from this issue of ‘Really Good Stuff’ refers to how much we can gain from our colleagues in different countries and from different cultures. I hope you find something that challenges your thinking, even if you don’t agree with the ideas presented, and that you enjoy what you read. Selecting which reports to publish from among the submissions continues to represent a challenge, for there is much really good stuff submitted. Lessons for authors include the need to ensure that if English is not your native language, you ask someone who speaks English well to read the report and adjust the writing before it is submitted. Some very good reports cannot be accepted because they are not written clearly enough to enable the reader to fully understand what was done and what was learned. One report in this collection documents an idea that was not successful and the lessons learned from that experience. Please consider submitting reports of ideas that did not work if the experience gave you valuable lessons that you would like to share. Thank you to all of the authors who have willingly shared their work, and to all of the reviewers for their time and energy. My particular thanks go to Sue Symons and Amanda Dove for their support in making ‘Really Good Stuff’ happen.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.132
GPT teacher head0.465
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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