MétaCan
Menu
Back to cohort
Record W2735041370 · doi:10.12968/jowc.2017.26.sup7.s4

Patients' and clinicians' experiences of wound care in Canada: a descriptive qualitative study

2017· article· en· W2735041370 on OpenAlexaffabout
Woo K.Y., Josephine Wong, Kathleen Rice, Sara Coelho, E. Haratsidis, L. Teague, Valeria E. Rac, Murray Krahn

Bibliographic record

VenueJournal of Wound Care · 2017
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsWomen's College HospitalToronto Public HealthHome and Community Care Support ServicesQueen's University
Fundersnot available
KeywordsMedicineWound carePsychosocialConcordanceNursingQualitative researchMultidisciplinary approachHealth careNonprobability samplingContent analysisAffect (linguistics)Family medicinePsychologyPsychiatrySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: This study sought to explore patients' and clinicians' perceptions and experiences with the provision of standard care by a home care nurse alone or by a multidisciplinary wound care team. METHOD: The interviews were conducted using an in-depth semi structured format; following a funnel idea of starting out broad and narrowing down, ensuring that all the necessary topics were covered by the end of the interview. RESULTS: A purposive sample of 16 patients with different wound types were interviewed to ensure that the data would reflect the range and diversity of treatment and care experience. To reflect the diversity of experiences 12 clinicians from various clinical backgrounds were interviewed. Based on the analysis of the interviews, there are four overarching themes: wound care expertise is required across health-care sectors, psychosocial needs of patients with chronic wounds are key barriers to treatment concordance, structured training, and a well-coordinated multidisciplinary team approach. CONCLUSION: Results of this qualitative study identified different barriers and facilitators that affect the experiences of community-based wound care.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.377
Teacher spread0.332 · 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 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

Citations9
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Wound CareSame topicWound Healing and TreatmentsFrench-language works237,207