MétaCan
Menu
Back to cohort
Record W75450590 · doi:10.1177/216507990405201005

Implementation of a Scheduled Toileting Program in a Long Term Care Facility: Evaluating the Impact on Injury Risk to Caregiving Staff

2004· article· en· W75450590 on OpenAlexaff
Chris Engst, Rahul Chhokar, Dan Robinson, Ann Earthy, Annalee Yassi

Bibliographic record

VenueAAOHN Journal · 2004
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British Columbia HospitalFraser HealthCoquitlam College
Fundersnot available
KeywordsToiletingWorkloadMedicineStaffingIntervention (counseling)Nursing AssistantLong-term careOccupational safety and healthPoison controlInjury preventionNursingPhysical therapyMedical emergencyActivities of daily livingNursing homes

Abstract

fetched live from OpenAlex

This study evaluated the impact of a scheduled toileting program on the risk of injury to caregivers and on resident agitation or aggressive behaviors. Injury data, ergonomic assessments, staff questionnaires, and resident agitation checklists were used to evaluate the program in a 75 bed unit, with a similar unit acting as a comparison. The program resulted in an increased percentage of residents toileted regularly in the intervention unit, while aggressive incidents declined in both groups. Staff in the intervention unit reported a significantly lower perceived risk of injury to the head and neck than the comparison group. Although the program resulted in increased workload to manage multitasking, monitor an additional aspect of scheduled care, and perform more toileting transfers, overall risk of physical injury was reduced. The toileting program, a shift toward resident focused care, and enhanced agitation awareness combined to reduce resident handling injuries and resident agitation expressed as verbal behaviors or emotional upset, but not as physical behaviors. Clear communication, mentoring, and monitoring were important for successfully changing care practices.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.669

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 communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.503
Teacher spread0.458 · 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

Citations14
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueAAOHN JournalSame topicGeriatric Care and Nursing HomesFrench-language works237,207