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Record W2109648955 · doi:10.2182/cjot.05.0011

Pressure Ulcers and Occupational Therapy Practice: A Canadian Perspective

2005· article· en· W2109648955 on OpenAlexaffvenueabout
Ed Giesbrecht

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychological interventionMedicineOccupational therapyIntervention (counseling)ReferralPerspective (graphical)NursingClinical PracticeFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational therapists are involved in the prevention and management of pressure ulcers. While many therapists identify challenges with their clinical practice in this area, there is little information available regarding specific occupational therapy interventions or effectiveness. PURPOSE: The goal of this study was to gain a national perspective of practice in Canada and identify directions and trends in the management of pressure ulcers. METHOD: A survey was distributed to occupational therapists at 75 facilities across Canada, soliciting information on intervention, practice issues and satisfaction with their practice. RESULTS: Thirty-seven therapists from 36 facilities responded, answering questions about risk assessment tools, referral patterns, interventions and decision-making models, satisfaction with practice, intervention rationales, and recommendations for change. Risk assessment, early intervention and continuity of care were identified as concerns, with a lack of resources identified as a limitation to practice. Seating and positioning were common interventions. PRACTICE IMPLICATIONS: This information can be used by clinicians to direct best practice and professional development goals. Further research is indicated in risk assessment, team collaboration and mattress prescription.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.173
GPT teacher head0.488
Teacher spread0.314 · 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
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

Citations15
Published2005
Admission routes3
Has abstractyes

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