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Record W2017152449 · doi:10.1097/ans.0b013e3181cd834d

Applying Dixon and Dixon's Integrative Model for Environmental Health Research Toward a Critical Analysis of Childhood Lead Poisoning in Canada

2010· article· en· W2017152449 on OpenAlexaffabout
Amélie Perron, Kelly O'Grady

Bibliographic record

VenueAdvances in Nursing Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVulnerability (computing)Context (archaeology)Occupational safety and healthLead poisoningHealth careHuman factors and ergonomicsPoison controlSuicide preventionSociologyPsychologyPolitical scienceEnvironmental healthMedicineGeographyComputer scienceComputer securityPsychiatry

Abstract

fetched live from OpenAlex

In Brief Occurrences of childhood lead poisoning resulting from exposure to residential sources of lead is an underresearched area in Canada. Dixon and Dixon's Integrative Model for Environmental Health Research substantiates this claim by grouping Canadian research on this health topic into the model's 4 domains: physiological, vulnerability, epistemological, and health protection. This process is useful not only for identifying research gaps within the Canadian context but also in setting the groundwork for a future critical analysis to illuminate the sociopolitical and economic influences that shape healthcare knowledge, and ultimately, influence how healthcare providers and policy makers produce and use this information. Dixon and Dixon's Integrative Model for Environmental Health Research sets the groundwork for a future critical analysis to illuminate the sociopolitical and economic influences that shape healthcare knowledge and, ultimately, influence how healthcare providers and policy makers produce and use research findings related to childhood lead poisoning.

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)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0150.023
Scholarly communication0.0110.005
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.440
Teacher spread0.400 · 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 designQualitative
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

Citations7
Published2010
Admission routes2
Has abstractyes

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