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Record W4408981078 · doi:10.1111/iwj.70179

Drop‐In Wound Care: Calgary's Wound Care Model Centred Around People Experiencing Homelessness

2025· article· en· W4408981078 on OpenAlexaffabout
Wisoo Shin, Mustafa Dahchi, Jennifer Laird, Rinna Lamano, Kelly D. Sair, Eileen Emmott, Laurie Parsons

Bibliographic record

VenueInternational Wound Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDisadvantagedWound careHealth careMultidisciplinary approachEmergency departmentAmbulance serviceFamily medicineNursingMedical emergencyEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

People experiencing housing insecurities or homelessness face significant barriers to equitable healthcare. A drop-in wound care service was established to mitigate social barriers and improve accessibility. This model facilitates direct access to a multidisciplinary team of trauma-informed medical staff on a walk-in basis. A retrospective chart review was performed on patients seen at the drop-in clinic from January 2021 to December 2021. A total of 119 patients were serviced over 798 visits, with 254 unique wounds managed. 82.8% of patients were living unsheltered, in emergency shelters or in provisional accommodation at the time of assessment. Trauma wounds, lower leg ulcers and frostbites represented the top three complaints. 69.7% of all patients returned to service for at least a second visit, with a median of 4 visits per patient over 42.5 days. Unsheltered patients were most likely to return to service (87.5%) but were most likely to be lost prior to wound closure (68.8%). Timely access to care with consistent follow-up is essential for quality wound care. Our drop-in service presents a working model for providing equitable wound care to socially disadvantaged patient populations. The effectiveness of this model is highlighted by the continual expansion serving 909 and 1029 visits in subsequent years.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.380
Teacher spread0.351 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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