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Record W1487989804 · doi:10.1111/wrr.12241

Structure and characteristics of community‐based multidisciplinary wound care teams in <scp>O</scp>ntario: An environmental scan

2014· article· en· W1487989804 on OpenAlexafffundabout
Lusine Abrahamyan, Josephine Wong, Ba’ Pham, Gina Trubiani, Steven Carcone, Nicholas Mitsakakis, Laura G. Rosen, Valeria E. Rac, Murray Krahn

Bibliographic record

VenueWound Repair and Regeneration · 2014
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsInstitute for Work & HealthToronto General HospitalUniversity of TorontoUniversity Health NetworkWestern UniversityToronto Public Health
FundersOntario Ministry of Health and Long-Term Care
KeywordsMultidisciplinary approachMultidisciplinary teamWound careMedicineService (business)Health careNursingFamily medicineIntensive care medicineBusiness

Abstract

fetched live from OpenAlex

Multidisciplinary team approach is an essential component of evidence-based wound management in the community. The objective of this study was to identify and describe community-based multidisciplinary wound care teams in Ontario. For the study, a working definition of a multidisciplinary wound care team was developed, and a two-phase field evaluation was conducted. In phase I, a systematic survey with three search strategies (environmental scan) was conducted to identify all multidisciplinary wound care teams in Ontario. In phase II, the team leads were surveyed about the service models of the teams. We identified 49 wound care teams in Ontario. The highest ratio of Ontario seniors to wound team within each Ontario health planning region was 82,358:1; the lowest ratio was 14,151:1. Forty-four teams (90%) participated in the survey. The majority of teams existed for at least 5 years, were established as hospital outpatient clinics, and served patients with chronic wounds. Teams were heterogeneous in on-site capacity of specialized diagnostic testing and wound treatment, team size, and patient volume. Seventy-seven percent of teams had members from three or more disciplines. Several teams lacked essential disciplines. More research is needed to identify optimal service models leading to improved patient outcomes.

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)0.000
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 score0.032
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations11
Published2014
Admission routes3
Has abstractyes

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