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Record W1840317030 · doi:10.3109/09593985.2015.1060657

The impact of increased weekend physiotherapy service provision in critical care: a mixed methods study

2015· article· en· W1840317030 on OpenAlexaff
Catharine Duncan, Megan Hudson, Carol Heck

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPhysical therapyMedicinePhysical medicine and rehabilitationService (business)Business

Abstract

fetched live from OpenAlex

BACKGROUND: At the hospital studied, weekend physiotherapy (WEPT) is routinely provided and in 2013 WEPT was increased from one (PRE) to three (POST) physiotherapists (PTs) to cover intensive care and ward patients. AIMS: (1) To evaluate the impact of increased WEPT on patient volumes, treatments provided and conditions treated in critical care and wards; and (2) to understand the PTs' perspectives on the new coverage model. METHODS: A mixed methods design was utilized. The quantitative component consisted of retrospective document reviews of all weekend patients treated January 1-May 5 (PRE) and May 11-December 31 (POST). The qualitative component used a questionnaire to collect staff feedback. PRE-POST comparisons were conducted using χ(2) or Mann-Whitney U tests. RESULTS: Significant (p = 0.00) increases POST were seen in number of patients treated, number of mobility treatments provided and number of post-surgical patients seen in both clinical areas. The majority of survey respondents reported feeling adequately trained, but had concerns regarding the impact of increased WEPT on work-life balance. CONCLUSION: PTs perceived enhanced service was beneficial for continuity of weekday care and improved patient function. Future studies need to focus on measuring the effect of increased weekend provision on outcomes, preventing complications and length of stay.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.516
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 head0.040
GPT teacher head0.509
Teacher spread0.469 · 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
Published2015
Admission routes1
Has abstractyes

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