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Record W2903834336 · doi:10.1097/jfn.0000000000000225

The Suitability of Grounded Theory Research for Correctional Nursing

2018· article· en· W2903834336 on OpenAlexaff
Kirnvir K. Dhaliwal, Kathryn King‐Shier, Sandra P. Hirst

Bibliographic record

VenueJournal of Forensic Nursing · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsForensic nursingNursing practiceNursingGrounded theorySubspecialtyProcess (computing)Nursing theoryMedicineNursing researchPsychologyMEDLINEQualitative researchSociologyPoison controlComputer sciencePsychiatryLawPolitical science

Abstract

fetched live from OpenAlex

The tension between custody and caring is particular to correctional nursing practice, including issues such as the correctional priorities of safety and security that often dominate nursing practice. The evidence that should underlie correctional nursing practice is regrettably sparse. There are two reasons a grounded theory (GT) approach is paramount for building foundational knowledge to advance correctional nursing practice. First, the development of theories regarding correctional nursing practice will provide an in-depth understanding of this subspecialty of nursing and lead to further research endeavors. Second, correctional nursing practice is a process carried out in correctional institutions. The GT approach is "process oriented" and thus is appropriate for exploring the implementation of correctional nursing practice. Two GT approaches have evolved since first described by Glaser and Strauss. We contend that the approach offered by Strauss and Corbin may be more beneficial for studying how correctional nurses implement their practice.

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)0.202
metaresearch head score (Gemma)0.296
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.798
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2020.296
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0130.019
Science and technology studies0.0070.026
Scholarly communication0.0220.020
Open science0.0050.009
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.096
GPT teacher head0.458
Teacher spread0.362 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations3
Published2018
Admission routes1
Has abstractyes

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