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Record W4400523438 · doi:10.37964/cr24782

How coaches can engage clients to empower performance

2024· article· en· W4400523438 on OpenAlexvenueaboutno aff
Debrah Wirtzfeld

Bibliographic record

VenueCanadian Journal of Physician Leadership · 2024
Typearticle
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingPsychologyBusinessProcess managementPublic relationsPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

The Canadian Physician Coaches Network (CPCN) is a not-for-profit organization whose coach members serve the needs of clients who are physicians. All members have previous experience working with physicians and recognize the unique challenges that come with being a member of the medical profession. Because engaging with a coach is an important aspect of achieving full leadership ability,1-3 the CPCN has chosen to come together with the Canadian Society of Physician Leaders to produce a quarterly Coaching Corner. Each article explores a potential physician leadership challenge and how a coach might engage with a client to assist them in empowering their performance. This article uses a fictitious person in a realistic scenario with the author as the coach.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0080.004
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.003

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 head0.165
GPT teacher head0.338
Teacher spread0.172 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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