MétaCan
Menu
Back to cohort
Record W2399665614 · doi:10.1177/0733464816649278

Care Challenges in the Bathroom: The Views of Professional Care Providers Working in Clients’ Homes

2016· article· en· W2399665614 on OpenAlexafffund
Emily C. King, P. J. Holliday, Gavin J. Andrews

Bibliographic record

VenueJournal of Applied Gerontology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcMaster UniversityToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchGovernment of OntarioUniversity of TorontoToronto Rehabilitation InstituteMcMaster University
KeywordsToiletingBathingNursingShowerBathtubSet (abstract data type)PsychologyMedicineActivities of daily livingComputer sciencePhysical therapyEngineering

Abstract

fetched live from OpenAlex

In home care, bathroom activities-particularly bathing and toileting-present a unique set of challenges. In this focus group study, professional home care providers identified factors that increase the danger and difficulty of assisting their clients with bathing and toileting. These included small restrictive spaces, a poor fit between available equipment and the environment, a reliance on manual handling techniques (but insufficient space to use optimal body mechanics), attempts to maintain normalcy, and caring for unsteady and unpredictable clients. Specific elements of each activity that care providers found difficult included multitasking to support client stability while performing care below the waist (dressing/undressing, providing perineal care) and helping clients to lift their legs in and out of a bathtub. Participants did not feel that available assistive devices provided enough assistance to reduce the danger and difficulty of these activities.

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)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0030.004
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 head0.125
GPT teacher head0.402
Teacher spread0.277 · 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 designQualitative
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

Citations35
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied GerontologySame topicGeriatric Care and Nursing HomesFrench-language works237,207