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Enregistrement W214194437

Trends in collection, use and disclosure of personal information in contemporary health research: challenges for research governance.

2005· article· en· W214194437 sur OpenAlexaffabout
Donald J. Willison

Notice bibliographique

RevuePubMed · 2005
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHealth disparities and outcomes
Établissements canadiensSt. Joseph’s Healthcare Hamilton
Organismes subventionnairesnon disponible
Mots-clésHealth careHealth policyHRHISPublic healthHealth equityEnvironmental healthPublic health informaticsHealth educationInternational healthPublic relationsObservational studyHealth services researchPsychologyMedicinePolitical scienceNursingEconomic growthEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: Changes in the Nature and Use of Personal Information Health Research Health research encompasses a heterogeneous set of research activities. This paper focuses on challenges that arise in the governance of observational research which is usually carried out without any direct contact between the researcher and the individuals being studied. Two broad areas of health research are heavily dependent on access to a wide range of existing person-level health information: 1. Public health, occupational health and safety, and the non-medical determinants of health and disease. The latter examines the relationship between health and lifestyle, environmental, and socioeconomic factors including income and education. Epidemiology is the foundation of much of this type of research. Research in this domain links both health and non-health information, such as occupation, education, and lifestyle information. 2. Health policy, health services research, and program evaluation examine the health care system and the effects of different policies and methods of health care delivery on the quality and efficiency of care provided. This type of research is informed by a wide variety of disciplines, including: economics, health policy, political sciences, sociology, anthropology, medicine, and epidemiology. Most health research requires person-level data, chiefly to increase precision in analysis. For example, when trying to determine the effect of exposure to an environmental toxin in a neighbourhood, with person-level data one can better examine the causal relationship by controlling for or holding constant known personal factors such as age and sex of the individual that relate to the outcome of interest. Similarly, when evaluating a policy to increase co-payments prescription drugs, it is prudent to examine across different income brackets the impact of that policy on the tendency to discontinue medications. In some cases, if using aggregate rather than individual-level data, it is possible to come to spurious conclusions about the effect of exposure (whether to a policy or an environmental toxin) on health outcomes. (1) Also, individual-level data are required to link information from disparate databases. This linkage creates the ability researchers to answer a much broader set of questions about the determinants of health, but it also raises major privacy concerns when these activities are being conducted without individual consent. Although data may be stripped of direct personal identifiers, the resultant records are often so rich in information that the residual risk of disclosure of identity through indirect means is sufficiently high that the data must be treated as if they were identifiable. In fact, with as little information as date-of-birth, sex, and full postal code, the majority of individuals in a particular region may be re-identified by linking with census tract information. (2) Trends in Data Collection, Use, Storage, and Disclosure Twenty years ago, only a handful of research centres across North America had the capacity to manipulate and link large data sets, and most government and other data repositories were used only claims adjudication. Medical records were all paper-based. Advances in the capacity of computers and the internet to store, manipulate and disseminate large amounts of data have changed dramatically the nature of collection, use and disclosure of personal information in contemporary health research. These advances have spawned two parallel developments: the planning and development of large disseminated health information networks that will serve multiple purposes beyond those direct clinical care; and the proliferation of decentralized holdings of personal data. In Canada, the United States, much of the European Union, Australia, and New Zealand, major efforts are underway to computerize patient records across health care settings, with the ability to share and link information from the records of physicians, diagnostic facilities, and health care institutions. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,535
score de la tête « metaresearch » (Gemma)0,694
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,465
Score d'incertitude au seuil0,573

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,5350,694
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0110,033
Études des sciences et des technologies0,0060,026
Communication savante0,0270,036
Science ouverte0,0080,017
Intégrité de la recherche0,0110,017
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,532
Tête enseignante GPT0,467
Écart entre enseignants0,064 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2005
Routes d'admission2
Résumé présentoui

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