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Record W4284714470 · doi:10.1097/jnn.0000000000000665

Effect of the Preparatory School FAST Stroke Educational Program

2022· article· en· W4284714470 on OpenAlexaff
Abdul Salam, Ashfaq Shuaib, Saadat Kamran, Iman Hassanin, Nosheen Shahid, Mohammed S. Al-Darwish, Rubina Bibi, Maher Saqqur, Numan Amir, Elaine Miller

Bibliographic record

VenueJournal of Neuroscience Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMuscular Dystrophy CanadaSouth Health Campus
Fundersnot available
KeywordsStroke (engine)MedicineEducational programIntervention (counseling)Physical therapyFamily medicineNursing

Abstract

fetched live from OpenAlex

ABSTRACT: BACKGROUND: The aim of this study was to assess the effectiveness of FAST stroke educational program among all preparatory school students in the state of Qatar. METHODS: The pretest-posttest experimental research design was used to evaluate the effectiveness of the FAST educational program in Qatar. A 30-minute audiovisual presentation was given to improve knowledge of stroke. We included grade 7 to 9 students during the academic year 2018-2019. The FAST program consisted of a pretest, an educational intervention, and immediate and long-term posttests at 2 months. RESULTS: A sample of 1244 students completed presurvey and immediate postsurvey, with an average age of 13.5 (1.12) years (range, 11-18 years) and 655 (53%) females. Students had significantly ( P < .01) greater knowledge of stroke signs, symptoms, and risk factors at intermediate posttest (5.9 [2.6] and 6.2 [2.4]) and at 2 months posttest (5.6 [2.8] and 5.6 [2.7]) compared with pretest (4.8 [2.6] and 4.9 [2.6], respectively). Students also had a higher self-efficacy to seek assistance, which was sustained from pretest to long-term posttest. CONCLUSION: The FAST program improved stroke knowledge that was retained at 2 months.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.333
Teacher spread0.322 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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