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Record W2761279306 · doi:10.1093/pch/19.6.e35-128

130: Inequity Amongst Children's Developmental Conditions: Marketing Tactics in a Crowded Field

2014· article· en· W2761279306 on OpenAlexaff
S D'Agostini, Jennifer Mateshaytis, Jeffrey S. Harrison, Lynn McIntyre

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContent analysisRanking (information retrieval)The InternetMedicineHealth informationPsychologyHealth careWorld Wide WebComputer scienceInformation retrieval

Abstract

fetched live from OpenAlex

Evidence suggests that parents use the Internet to find information and resources for their children's health condition, yet web presence differs and some health conditions sell their information and resources more effectively than others. The purpose of this study was to compare the web content available in the google domain for five pediatric developmental disorders and to determine the characteristics and implications of these differences for health information and health service access. Research was conducted on five pediatric developmental disorders: CF, FXS, Down Syndrome (DS), Duchenne Muscular Dystrophy (DMD), and Spina Bifida (SB). These disorders were chosen because they each are: lifelong, incurable, multi-systemic and genetically-based. Content analysis was conducted using a coding sheet that allowed for consistent evaluation of the Google domain content retrieved via standardized search queries. Content analysis was conducted on pre-set dates and times, with the top 30 links being ranked and evaluated. Significant differences were seen between the disorders with respect to total number of hits, website category and website content. Averaged over two research sessions, DS and CF accumulated more hits overall (101,300,000 and 23,500,000 respectively) with the other disorders totaling to less than 5,100,000 hits each. For website category, CF and SB were found to have a higher proportion of National Organizations ranking in the top 30 hits; DS had the greatest proportion of local organizations, while DMD and FXS had the majority of their top 30 hits dedicated to health informational databases. A subjective analysis of tone was performed on the top 30 hits; >65% of the websites for both CF and DS depicted an overarching positive tone, while at least 50% of each of SB, DMD and FXS's hits were neutral. Of note, websites targeted the public as their audience as opposed to those affected by the condition. Although these disorders share many attributes, they present vast differences in both their web presence and web content. CF and DS present a more well-rounded and positive approach to the disease, while FXS, SB and DMD focus more on dissemination of disorder- related information. These disparities in resources and access to information among disorders hold many implications for practicing physicans as well as the health care system at large. Physicians should be mindful that more activist disorders may receive inequitable attention from the health care system. Improvements to tools and information are necessary to provide reliable and useful online resources to families for FXS, SB and DMD.

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)0.010
metaresearch head score (Gemma)0.024
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: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0060.005
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.375
Teacher spread0.353 · 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
GenreCommentary

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
Published2014
Admission routes1
Has abstractyes

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