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Record W2121675374 · doi:10.1111/iwj.12388

Achieving competency in wound care: an innovative training module using the long‐term care setting

2015· article· en· W2121675374 on OpenAlexaff
Evelyn Williams, Susan Deering

Bibliographic record

VenueInternational Wound Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCurriculumDocumentationWound careGeriatricsLong-term careSession (web analytics)Medical educationNursingFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Structured academic teaching on wound care was developed, based on the long-term care (LTC) setting, with the goal of ensuring that postgraduate family medicine residents attain competency in assessment and treatment of wounds and pressure ulcers (PUs). The curriculum for the 1-month learning module was based on clinical practice guidelines for the prevention, assessment, and treatment of PUs and wounds. The learning techniques used include a learners' needs assessment, a small-group didactic session, interdisciplinary bedside case discussions and a toolkit. The curriculum is delivered in four weekly, 90-minute interdisciplinary teaching sessions during the mandatory 1-month geriatrics rotation for postgraduate family medicine trainees. Competency is evaluated by the end of the module by reviewing trainees' documentation of a thorough objective clinical wound assessment, diagnosis of underlying cause, significant contributing risk factors and proposed treatment plan. This approach can be used to train family medicine, hospitalist, and geriatric residents in other acute or LTC teaching facilities where there is a prevalence of PUs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.122
GPT teacher head0.450
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2015
Admission routes1
Has abstractyes

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