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Record W2801953275 · doi:10.1007/s11469-018-9904-x

Protection of Privacy of Information Rights among Young Adults with Developmental Disabilities

2018· article· en· W2801953275 on OpenAlexafffundabout
Nazilla Khanlou, Anne Mantini, Attia Khan, Katie Degendorfer, Masood Zangeneh

Bibliographic record

VenueInternational Journal of Mental Health and Addiction · 2018
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsUniversity of Guelph-HumberYork University
FundersOffice of the Privacy Commissioner of CanadaAdministration for Community LivingMental Health Commission
KeywordsHealth psychologyPublic healthPsychologyInternet privacyEnvironmental healthMedicineNursingComputer science

Abstract

fetched live from OpenAlex

Protection of privacy of information for young adults with developmental disabilities and their families is essential to promote quality of life, well-being, empowerment, and inclusion. Despite this, the young adults' information privacy rights are increasingly at risk. This paper provides a scoping review, applying Arksey and O'Malley's (2005) approach, of all published peer-reviewed journal articles and gray literature to examine the barriers and facilitators in utilization of legislation that protects the collection, use, disclosure, and access of personal information in Canada. The scoping review process was further expanded with a rigorous reliability method and applied a socio-ecological framework to the final 47 studies. National and international policy and legislation (macro level), organization-based factors (meso), young adults and community interactions (exo), and individual disability related factors (micro) are examined. The review identifies the barriers and highlights the importance of facilitators for acting on personal privacy rights.

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)0.009
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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 designObservational
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

Citations10
Published2018
Admission routes3
Has abstractyes

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