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Formal Caregiver Experiences of Caring for Individuals at Risk for or with a Pressure Injury: A Metasynthesis

2022· review· en· W4288068208 on OpenAlexaff
Corey Heerschap, Kevin Woo

Bibliographic record

VenueAdvances in Skin & Wound Care · 2022
Typereview
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsRoyal Victoria Regional Health CentreWestern University
Fundersnot available
KeywordsCINAHLMedicinePsychological interventionMEDLINENursingCLARITYHealth carePressure injuryMedical education

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore formal caregiver experiences caring for patients with a pressure injury (PI) or who are at risk of developing a PI. DATA SOURCES: In November 2019, the researchers searched CINAHL (Cumulative Index to Nursing and Allied Health Literature) and MEDLINE databases for articles related to caregivers and their experiences with PI prevention and care. STUDY SELECTION: Twenty-eight qualitative studies were included in this review. DATA EXTRACTION: Literature was graded and critiqued with regard to design and research quality and then synthesized utilizing a narrative approach. DATA SYNTHESIS: Four themes were found within the literature: knowledge and education, environment and resources, collaboration and role clarity, and risk assessment. CONCLUSIONS: Across healthcare settings, formal caregivers noted the importance of effective PI knowledge and education. Recognizing both barriers and facilitators to PI prevention and management within the healthcare environment can help decision-makers make informed choices to improve PI management within their settings. In addition, developing interprofessional team skills and relationships, rather than practicing in silos, may have an impact on PI management. Although there are many interventions that reduce PI risk and assist in the management of PIs, not every intervention is appropriate for every healthcare setting. Clinician education on PIs, along with new interventions, could significantly impact the effectiveness of patient care.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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 score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.437
Teacher spread0.380 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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