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Record W2462835278 · doi:10.1097/jfn.0000000000000097

Caring in Correctional Nursing

2016· review· en· W2462835278 on OpenAlexaff
Kirnvir K. Dhaliwal, Sandra P. Hirst

Bibliographic record

VenueJournal of Forensic Nursing · 2016
Typereview
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsForensic nursingNursingContext (archaeology)NarrativeHealth carePsychologyMedicinePoison controlPolitical scienceMedical emergency

Abstract

fetched live from OpenAlex

Registered nurses are the primary healthcare providers for offenders in correctional facilities. The way in which correctional nurses care for offenders can be difficult in this context. Following a systematic review and narrative synthesis of literature regarding how correctional nurses show caring for offenders three themes emerged: the struggle of custody and caring (conflicting ethical and philosophical ideologies, correctional priorities that override nursing priorities, safety and security), the need to be nonjudgmental (judgmental attitudes can impact care; focus on health not the crime), and the importance of boundaries. Implications for practice focus on recommendations to promote caring in correctional nursing; the outcome of which will potentially enhance quality of care for offenders and improve working environments for nurses.

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)0.003
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.411
Teacher spread0.353 · 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
GenreReview

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

Citations97
Published2016
Admission routes1
Has abstractyes

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