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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 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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
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 head0.096
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; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations3
Published2018
Admission routes1
Has abstractyes

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