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Record W7100823751

25CONJ • 18/1/08 RCSIO • 18/1/08

2016· article· en· W7100823751 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLegal Cases and Commentary
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupHealth carePopulationNursing careQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Care maps for patient care have been around for many years. Key stakeholders at our institution developed and implemented a care map for patients undergoing surgery for colorectal cancer. The purpose of this descriptive, qualitative pilot study was twofold. First was to understand the lived experience of patients being cared for under a newly-implemented care map utilizing patient diaries and interviews. The second goal was to describe the experiences of surgical oncology nurses caring for these patients using a focus group technique. The results of our small study indicated that patients appreciated having a document that outlines daily activities and goals, and were anxious to get home, but were disappointed in the discharge planning process. Nurses were positive about the care map overall, but felt they could have contributed more in the development and planning stages of the care map. Overall, the implementation of our patient-centred care map was a success. Publicly-funded health care systems, such as those in Canada, face increasing pressures to simultaneously harness the soaring costs associated with providing care to those who need it and providing that care in a timely manner. Since the new millennium, a key area of focus in Ontario has been reducing surgical “wait times ” in various populations, including those patients with cancer. Recent literature in the colorectal surgery population has suggested a median length of stay (LOS) of two days can be achieved (Basse, Hjort Jakobsen, Billesbolle,

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

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)0.0350.002

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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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