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Record W4220811146 · doi:10.1186/s12890-022-01878-3

Implementation of a strategy to facilitate effective medical follow-up for Australian First Nations children hospitalised with lower respiratory tract infections: study protocol

2022· article· en· W4220811146 on OpenAlexaffabout
André Schultz, Anne B. Chang, Fenella J. Gill, Roz Walker, Melanie Barwick, Sarah Munns, Matthew N. Cooper, Richard Norman, Pamela Laird

Bibliographic record

VenueBMC Pulmonary Medicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersWestern Australian Health Translation NetworkGovernment of Western AustraliaDepartment of Health, Government of Western AustraliaChildren's Hospital Foundation
KeywordsMedicineBronchiectasisBronchitisIntervention (counseling)Randomized controlled trialRespiratory tract infectionsQuality of life (healthcare)Health carePediatricsFamily medicineIntensive care medicineNursingRespiratory systemSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: First Nations children hospitalised with acute lower respiratory infections (ALRIs) are at increased risk of future bronchiectasis (up to 15-19%) within 24-months post-hospitalisation. An identified predictive factor is persistent wet cough a month after hospitalisation and this is likely related to protracted bacterial bronchitis which can progress to bronchiectasis, if untreated. Thus, screening for, and optimally managing, persistent wet cough one-month post-hospitalisation potentially prevents bronchiectasis in First Nations' children. Our study aims to improve the post-hospitalisation medical follow-up for First Nations children hospitalised with ALRIs and thus lead to improved respiratory health. We hypothesize that implementation of a strategy, conducted in a culturally secure manner, that is informed by barriers and facilitators identified by both parents and health care providers, will improve medical follow-up and management of First Nations children hospitalized with ALRIs. METHODS: Our trial is a multi-centre, pseudo-randomized stepped wedge design where the implementation of the strategy is tailored for each study site through a combined Participatory Action Research and implementation science approach informed by the Consolidated Framework of Implementation Research. Outcome measures will consist of three categories related to (i) health, (ii) economics and (iii) implementation. The primary outcome measure will be Cough-specific Quality of Life (PC-QoL). Outcomes will be measures at each study site/cluster in three different stages i.e., (i) nil-intervention control group, (ii) health information only control group and (iii) post-intervention group. DISCUSSION: If our hypothesis is correct, our study findings will translate to improved health outcomes (cough related quality of life) in children who have persistent wet cough a month after hospitalization for an ALRI. Trial registration ACTRN12622000224729, prospectively registered 8 February 2022, URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382886&isReview=true .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.058
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.058
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.045
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0040.004
Open science0.0050.005
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0510.009

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 head0.310
GPT teacher head0.589
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations2
Published2022
Admission routes2
Has abstractyes

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