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Enregistrement W7020982601

Non-participation in clinical research: Barriers, motivators, and recruitment strategies in an ovarian cancer study

2017· dissertation· en· W7020982601 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueeScholarship@McGill (McGill) · 2017
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueHigh-Energy Particle Collisions Research
Établissements canadiensnon disponible
Organismes subventionnairesCanadian Institutes of Health ResearchMcGill University Health CentreVictoria General Hospital FoundationMcGill University
Mots-clésOvarian cancerIncidence (geometry)DiseaseCancerPopulationIntervention (counseling)Public health
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Clinical studies have made significant contributions to disease prevention, early diagnosis, and new therapies for many diseases, including cancer. However, recruitment of participants is a major challenge in clinical studies on cancer; older adults, minorities, rural residents, and individuals of lower socio-economic status are traditionally difficult to enroll, despite these groups having higher cancer incidence and mortality. The ongoing clinical study in Montreal (Canada)—Diagnosing Ovarian cancer Early (DOvE)—aims to determine whether providing fast-track diagnostic testing to symptomatic women over 50 leads to early diagnosis and better prognosis. Results from the pilot phase showed a 'high' prevalence of ovarian cancer among participants and indicated an increased likelihood of diagnosing ovarian cancer early while still completely resectable. However, these promising findings were observed in a highly selected population, with a higher proportion of younger, highly educated and Anglophone women, which raised the concern of volunteer bias. The benefits of the intervention could be overestimated if those who volunteered to participate in DOvE would have been diagnosed earlier even in the absence of DOvE (the study has no control group, given that all participants have symptoms). The overarching objective of this manuscript-based thesis is to examine different aspects of non-participation in clinical cancer studies. To characterize non-participation, it is essential to have knowledge about the underlying target population. However, in the case of DOvE, the target population was unknown (i.e. women aged 50 and older living in Greater Montreal and having symptoms with the specified duration). Thus, the first paper was to estimate the prevalence of symptoms determining eligibility to DOvE and their associations with socio-demographic characteristics. The object of the second paper was to identify factors associated with intention to participate and investigate motivators and barriers among potential DOvE participants. The third paper was to evaluate the effectiveness of strategies for increasing participation of underrepresented groups. Between May 2011 and April 2014, DOvE investigators opened five additional centers in areas with a dense population of older, Francophone women. The third manuscript examines the success of this strategy in terms of participant characteristics and study accessibility.A postal survey was sent to a random sample of 3000 women aged 50 and older living in Greater Montreal, with up to two reminders to non-responders. Due to the low response (28%), the inverse probability weighting was applied to correct for non-response. Older women (70+) were less likely to respond and, among the responders, less likely to report any symptom. Prevalence of symptoms was high, even when limiting duration to a specific time window (59.7 % reported at least one symptom), and the crude and weighted estimates were very similar. Having more symptoms and not having a family doctor was significantly associated with intention to participate, while being older, living in rural areas, having lower income and being in excellent health was associated with intention to not participate. The majority of those who did not plan to participate preferred to be examined by their own physician or thought that they were not at risk of ovarian cancer. "Inconvenience" was also a commonly reported reason. Strategically placing satellite centers across the city improved accessibility and resulted in a less selected study population.The findings of this thesis not only provide us with a better picture of the underlying population for DOvE, but also suggest several strategies with the potential of enhancing recruitment of hard-to-reach groups. Efforts to include the subgroups that disproportionally experience higher rates of cancer in clinical research continue to be an important issue in need of further research.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,346
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,149
Tête enseignante GPT0,463
Écart entre enseignants0,314 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
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

Citations0
Publié2017
Routes d'admission2
Résumé présentoui

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