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Record W2748971830 · doi:10.20431/2455-4324.0301002

Continence Training Needs Assessment of Residential Long-Term Care Personal Support Workers

2017· article· en· W2748971830 on OpenAlexaffabout
Deanne Taylor, Jacqueline Cahill

Bibliographic record

VenueARC Journal of Nursing and Healthcare · 2017
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Training (meteorology)Personal careLong-term careNeeds assessmentNursingBusinessMedicineFamily medicineGeography

Abstract

fetched live from OpenAlex

Urinary incontinence (UI) and fecal incontinence (FI) are embarrassing and debilitating conditions that are becoming collectively a major, growing health concern for men and women of all ages worldwide.Recent research showed that professional care-givers in long-term facilities (nursing homes) were not confident in their skills to care for FI residents.This long-term care residential-based study conducted in Ontario Canada was purposed to identify gaps in the knowledge of professional care-givers.Based upon the results of this study there appeared to be room for improvement in the management of both UI and FI in long-term care residences in Ontario.It is recommended that a first tranche of participatory education include topics such as: the causes of FI; the value of using assessments, histories and diaries in moving from treatment to management; dealing with residents that are incontinent as well as depressed.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.122
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.413
Teacher spread0.347 · 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 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueARC Journal of Nursing and Healthcare→Same topicPelvic floor disorders treatments→French-language works237,207→