MétaCan
Menu
Back to cohort
Record W2440866932 · doi:10.1097/nur.0000000000000217

Supporting and Empowering Nurses Undergoing Critical Care Certification

2016· article· en· W2440866932 on OpenAlexafffundabout
Geneviève Beaudoin, Lyne St‐Louis, Marie Alderson

Bibliographic record

VenueClinical Nurse Specialist · 2016
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsJewish General Hospital
FundersJewish General Hospital
KeywordsCertificationNursingMEDLINEMedicinePolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Nurses working in critical care settings face multiple sources of stress, such as complex clinical situations and the use of new advanced technologies, which can affect their psychological health. Literature suggests that the promotion of educational activities, such as a certification process within a specialty, can contribute to nurses' empowerment, professional growth, and personal satisfaction. However, it is of utmost importance that the institutional organizations support nurses undergoing the certification process to optimize positive impacts of this educational activity on the nurses, on the patients, and within the institutions. DESCRIPTION OF THE PROJECT: In the course of a graduate studies stage, an educational program aiming at supporting and creating an empowering environment for nurses undergoing a critical care certification process was developed and implemented, in a Canadian intensive care unit, over a 7-month period. The Humanist model was used as a theoretical framework and was complemented by the problem-based learning pedagogical approach. OUTCOMES: A postintervention qualitative questionnaire revealed that the program was tailored to nurses' learning needs and that participants felt supported by their institution throughout the process. CONCLUSION: This program could help institutions support nurses in achieving higher professional and personal development levels through specialty certification.

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)0.000
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.061
GPT teacher head0.458
Teacher spread0.397 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueClinical Nurse SpecialistSame topicNursing education and managementFrench-language works237,207