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Making it matter

2011· editorial· en· W1965626984 on OpenAlexaboutno aff
Steve Trumble

Bibliographic record

VenueThe Clinical Teacher · 2011
Typeeditorial
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumNothingMedical educationPoint (geometry)Medical schoolService (business)PsychologyMedicinePedagogyBusiness

Abstract

fetched live from OpenAlex

One of the most enjoyable aspects of refurbishing a long-established medical course is that a great deal of accreted curriculum content can be jettisoned to make room for more contemporary materials and methods. Unfortunately, it is easier to graft on new content than it is to prune the old. Every tiny twig of teaching is held dear by at least one person in the faculty, who will warn of dire consequences if their bit is not taught. Really new ideas are rare enough that their arrival causes a frisson of excitement amongst those who are redesigning the curriculum. Even more exciting, however, are ideas that bring a brand new aspect to medical education while also providing a useful service to the community. Educational activities that immediately lead to improved patient outcomes are at the pinnacle of what we are seeking to achieve. In Kirkpatrick’s well-known four-stage model,1 a medical course that achieves direct results for patients (rather than just equipping students with knowledge, skills and attitudes to use in the workplace) is to be celebrated. So it is somewhat ironic that one idea for clinical education that is gaining traction in Australia is neither particularly new nor anything much to do with the medical curriculum. It is the volunteer student-led clinic. At this point, North American readers will let out a snort of derision. Student-led clinics are nothing new in the USA and Canada, with just over half of US medical schools being associated with at least one of more than 100 such clinics.2 In many cases, student-led clinics provide the only services that disadvantaged groups can afford. Studies are emerging that show patients who attend these clinics do no worse – and in some cases do better – than others from similar disadvantaged backgrounds, or even than those with health insurance, on some measures.3, 4 Patients are satisfied with the service that they receive,5 and students learn an enormous amount about interprofessional health care and how it is delivered.6, 7 Because what they do actually matters. Student-led clinics do not appear to be prevalent in the UK’s NHS, although they may well have had their origins in such compassionate ventures as Lettsom’s General Dispensary in London’s Aldersgate Street, or Sir Andrew Duncan’s people’s dispensary in Edinburgh in the 1770s.8 These dispensaries were driven by remarkable doctors, however, whereas today’s student-led clinics are notable by their reliance on the students themselves to set them up, navigate the minefields of indemnity insurance, recruit volunteer supervisors and take frontline responsibility for managing patients. It is clear that those involved in establishing a student-led clinic learn many vital professional skills before the first patient even crosses the threshold. Interprofessionalism is another new concept in most medical curricula, and it struggles to find purchase when taught in abstraction. Role-plays, simulated emergencies and interprofessional team challenges are all worthwhile learning activities, but nothing compares with actually working in an interdisciplinary team of students that is directly and solely responsible for a real patient’s health care. It is early days yet for the clinic that is forming in Melbourne, but I was struck by an e-mail from a newly qualified nurse who is playing a leadership role in pulling it all together. ...from an interdisciplinary practice perspective, I have found that I have developed a greater awareness for the communication strategies I use with students of different disciplines, based on their professional and social values, in order to achieve the various outcomes we desire. This is a valuable skill for interdisciplinary practice – and we haven’t even opened the doors...yet! Editor in Chief

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.069
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.012
Insufficient payload (model declined to judge)0.0530.042

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 head0.379
GPT teacher head0.595
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2011
Admission routes1
Has abstractyes

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