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The assistance to families with cancer through the Calgary Model: contributions to the teaching of nursing in public health

2021· article· en· W3211243194 on OpenAlexaboutno aff
Claudelí Mistura, Keroli Fernanda Rodrigues Lima, Danielle Valim Pereira Leivas, Michelle Christini Araújo Vieira, Silvana Neumann Martins, Cláudia Mistura

Bibliographic record

VenueLAPLAGE EM REVISTA · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsnot available
Fundersnot available
KeywordsRelevance (law)NursingIntervention (counseling)Public healthPublic health nursingHealth careNurse educationMedical educationDescriptive researchMedicinePsychologySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The aim of this study is describing the importance of teaching the Calgary Model in nursing courses, to develop its application in the care of families with cancer in public health. This is a descriptive study of literature review about 10 national and international scientific works. The analysis of the results was performed in a qualitative and descriptive method. It became evidenced the relevance of the insertion of the Calgary Model in teaching, in nursing courses to promote active learning of students about the evaluation and intervention of families with cancer in public health, developing care under the Family Health Strategy. Thus, highlighting the importance of teaching focused on practices since the beginning of academic training so that students can be experiencing the reality.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.698
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.067
GPT teacher head0.404
Teacher spread0.337 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

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