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Record W2898399112 · doi:10.1002/nop2.200

Interpersonal touch interventions for patients in intensive care: A design‐oriented realist review

2018· review· en· W2898399112 on OpenAlexaff
Sansha J. Harris, Elizabeth Papathanassoglou, Melanie Gee, Susan Hampshaw, Lenita Lindgren, Annette Haywood

Bibliographic record

VenueNursing Open · 2018
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of Alberta
FundersNational Institute for Health and Care Research
KeywordsCINAHLPsychological interventionInterpersonal communicationMEDLINEIntervention (counseling)PsychologyHealth careGrey literatureEmpirical evidenceNursingMedicineApplied psychologySocial psychology

Abstract

fetched live from OpenAlex

AIM: To develop a theoretical framework to inform the design of interpersonal touch interventions intended to reduce stress in adult intensive care unit patients. DESIGN: Realist review with an intervention design-oriented approach. METHODS: We searched CINAHL, MEDLINE, EMBASE, CENTRAL, Web of Science and grey literature sources without date restrictions. Subject experts suggested additional articles. Evidence synthesis drew on diverse sources of literature and was conducted iteratively with theory testing. We consulted stakeholders to focus the review. We performed systematic searches to corroborate our developing theoretical framework. RESULTS: We present a theoretical framework based around six intervention construction principles. Theory testing provided some evidence in favour of treatment repetition, dynamic over static touch and lightening sedation. A lack of empirical evidence was identified for construction principles relating to intensity and positive/negative evaluation of emotional experience, moderate pressure touch for sedated patients and intervention delivery by relatives versus healthcare practitioners.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.176
GPT teacher head0.467
Teacher spread0.291 · 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 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

Citations12
Published2018
Admission routes1
Has abstractyes

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