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Record W4256184511 · doi:10.32920/ryerson.14668815

Nursing informatics and coaching based interventions: a protocol for delivery

2021· preprint· en· W4256184511 on OpenAlexaff
Suzanne Fredericks

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCoachingPsychological interventionProtocol (science)Intervention (counseling)InformaticsProcess (computing)NursingMedicineNursing Interventions ClassificationHealth informaticsMedical educationNursing processPsychologyKnowledge managementProcess managementComputer scienceEngineeringAlternative medicinePsychotherapist

Abstract

fetched live from OpenAlex

Coaching is a motivational approach often used to encourage the implementation of self-management educational instruction. It encompasses the processing, management, and retrieval of information. Coaching is not a common interaction routinely used in nursing care. This may be due to unfamiliarity with the interaction and/or lack of understanding of how to engage in coaching behaviours within the clinical setting. The purpose of this discursive paper was to present a detailed, step-by-step description of a protocol for delivering a coaching based intervention to patients following heart surgery and to discuss the process of delivering such interventions via nursing informatics. Conclusions drawn from this paper suggest that in order to enhance the application of technology in the delivery of coaching or support based interactions in the cardiovascular surgical environment, continued investigation to assess patients’ needs for technology and desirability to use technology as a tool to assist in their post-operative recovery experience is needed.

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)0.038
metaresearch head score (Gemma)0.039
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.039
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0650.033

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 head0.201
GPT teacher head0.515
Teacher spread0.313 · 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
GenreProtocol

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
Published2021
Admission routes1
Has abstractyes

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