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Record W2049019922 · doi:10.1136/ip.2010.029215.382

Understanding the injury prevention resource and learning needs of family resource centres

2010· article· en· W2049019922 on OpenAlexaffabout
P Fuselli, Susan J. Lockhart, Kathy Belton

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsResource (disambiguation)MedicinePopulationWork (physics)Medical educationNeeds assessmentFocus groupEnvironmental healthNursingPsychologyBusinessPolitical scienceEngineeringMarketing

Abstract

fetched live from OpenAlex

The Community Action Program for Children (CAPC) and the Canada Prenatal Nutrition Program (CPNP) offer support services to at-risk Canadian families with children ages 0–6 years. The objectives of this project are to assess the needs of CAPC/CPNP in terms of delivering programmes on injury prevention to families across Canada. Working through an Advisory Committee, we will complete a literature review on effectiveness of such programmes in reducing injuries, conduct surveys and meetings/focus groups to determine the needs of CAPC/CPNP as well as the families they serve. We will create a report which will include our key findings, as well as an analysis of the results, and our recommendations for future resource and training needs of CAPC/CPNP. The expected results of the project are, the CAPC/CPNP national office will have, within a population health approach A clear understanding of the current involvement of CAPC/CPNP projects across Canada in the prevention of childhood injury (activities undertaken and resources used). A clear understanding of diverse needs of these projects for resources and training to better equip them to address the prevention of injury in babies and young children with their participants. CAPC/CPNP projects, provincial and national injury prevention organisations will have a better understanding of each others programmes and resources and how they may better work together on this mutual goal.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.481
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.050
GPT teacher head0.329
Teacher spread0.280 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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