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Physician, heal thyself: tools for resident well‐being

2007· article· en· W2060942595 on OpenAlexaff
Jo Marie Reilly, Jeffrey M. Ring

Bibliographic record

VenueMedical Education · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsInternshipMedical educationContext (archaeology)ConversationHealth carePsychologyChecklistMedicineNursing

Abstract

fetched live from OpenAlex

Context and setting We have implemented an introductory workshop during internship orientation to introduce the concept of doctors as healers of themselves and their patients. Why the idea was necessary Medical training is incredibly stressful. Little attention is given to training doctors in self-care. We believe that teaching residents strategies for incorporating personal well-being into their daily lives is essential for longterm doctor wellness and should be part of residents' professional development. What was done We initiated an introductory workshop during orientation, co-facilitated by a doctor and a behaviourist faculty member, on the doctor's role as self-healer. The workshop serves as a catalyst for future discussions on self-care during residency training and plants seeds for personal well-being early on in the training process. First, the interns fill out the health awareness checklist that is given to clinic patients during physical examinations. Anonymously completed and self-reviewed, their answers stimulate a conversation about their personal self-care practices in personal nutrition and exercise, substance use and abuse, sexual health and safety, and overall emotional health. Together, we explore the notion that doctors who are more aware of their own health vulnerabilities and needs are more successful as healers of others. Next, the interns receive a pedometer as a personal fitness tool and are asked to record their steps for 1 month. The ‘step sheet’ is submitted to the faculty and tabulated each week during the first month of residency training. Consistent with the ‘Walk Across America Program’, the intern classes' total steps are calculated and marked with a shoe that indicates their collective progress towards personal fitness along a map of the USA. Wearing their pedometers in clinic serves both to model fitness to patients and to remind and motivate themselves to stay active and take care of themselves. Finally, the interns complete a personal wellness prescription. Here they list 3 commitments to themselves that will improve or maintain their physical and emotional well-being during residency training. These are made confidentially, sealed in envelopes and distributed to the intern's faculty advisor for further discussion. They are asked to re-visit their wellness prescription with their advisor on a bi-annual basis and modify it accordingly. Evaluation of results and impact Three intern classes (n = 21) have now completed the workshop on the doctor's role as self-healer. The data collected have been qualitative and highly positive. The most consistent feedback indicates that the interns feel valued and affirmed that faculty staff ‘care about them’ as individuals and doctor healers, independent of their clinical performance. Many appreciate the focus on raising their own self-care awareness, particularly at a very stressful time in their medical training. They appreciate the wellness prescriptions and their meetings with their advisors to check in on their self-care plan, feeling that this keeps them accountable for their well-being plans. Finally, the residents appreciate the pedometers as tools and reminders of personal fitness, especially as they have entered a training programme where consistent exercise is difficult to achieve. Some commented how patient inquiries about the pedometer have opened discussions about the importance of exercise.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.054
GPT teacher head0.495
Teacher spread0.441 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations4
Published2007
Admission routes1
Has abstractyes

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