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Record W2336420345 · doi:10.1155/2016/3185389

Building a Comprehensive System of Services to Support Adults Living with Long-Term Mechanical Ventilation

2016· article· en· W2336420345 on OpenAlexaffabout
David Leasa, Stephen Elson

Bibliographic record

VenueCanadian Respiratory Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineMemorandumMemorandum of understandingLong-term careContinuum of careIndependent livingNursingProcess managementService (business)Assisted livingService systemHealth careOperations managementGerontologyBusinessMarketing

Abstract

fetched live from OpenAlex

Background. Increasing numbers of individuals require long-term mechanical ventilation (LTMV) in the community. In the South West Local Health Integration Network (LHIN) in Ontario, multiple organizations have come together to design, build, and operate a system to serve adults living with LTMV. Objective. The goal was to develop an integrated approach to meet the health and supportive care needs of adults living with LTMV. Methods. The project was undertaken in three phases: System Design, Implementation Planning, and Implementation. Results. There are both qualitative and quantitative evidences that a multiorganizational system of care is now operational and functioning in a way that previously did not exist. An Oversight Committee and an Operations Management Committee currently support the system of services. A Memorandum of Understanding has been signed by the participating organizations. There is case-based evidence that hospital admissions are being avoided, transitions in care are being thoughtfully planned and executed collaboratively among service providers, and new roles and responsibilities are being accepted within the overall system of care. Conclusion. Addressing the complex and variable needs of adults living with LTMV requires a systems response involving the full continuum of 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.275
Teacher spread0.255 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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