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Record W2405647772 · doi:10.32920/ryerson.14668803.v1

Coaching in the cardiovascular surgical population.

2021· article· en· W2405647772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCoachingPopulationMedicinePsychologyEnvironmental healthPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: More than one quarter of all cardiovascular surgical patients are re-admitted to hospitals with postoperative complications experienced during the first three months of recovery. AIM AND METHOD: The purpose of this discursive paper is to review the literature pertaining to a self-management coaching intervention that is currently being evaluated using a randomized controlled clinical trial. RELEVANCE TO CLINICAL PRACTICE: A discussion of how to integrate coaching into clinical practice is presented. The use of coaching in the clinical setting has implications for nurses in that it can be used to assess behaviours, knowledge, and learning needs; provide individualized education that is reflective of a patient's identified learning needs; collaborate with patients in setting goals; identify barriers and engage in problem-solving to overcome barriers; and create a specific plan for follow-up.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.379
Teacher spread0.322 · 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

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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