Lessons Learned from the Recruitment Process of a South Asian Immigrant Women's Study: Analysis of Research Field Notes
Notice bibliographique
Résumé
Menopause, a normal transition in a woman’s life, is an individualized experience with varied attitudes and perceptions. In recent studies, menopausal symptoms and transitional experiences have been found to be greatly influenced by ethnic and cultural differences, as well as immigration. However, visible minority communities, like the South Asian immigrant population in Canada, remain underrepresented in women’s health research literature. The 1.9 million South Asians in Canada comprise 5.6% of the country’s total population and 25.1% of the visible minority population. As a result, there is great interest in improving the process of study recruitment for South Asian immigrant populations. In a menopausal transition study among South Asian immigrant women in the Greater Toronto Area, recruitment strategies were appraised in order to identify how to effectively and efficiently recruit ethnic minority groups for future women’s health studies. The study included all self-reported South Asian women who were: (a) A Canadian citizen or permanent resident; (b) Between the ages of 45 to 55; (c) Born outside of Canada; and (d) Living in Canada for no more than 20 years. A research assistant of South Asian descent recruited the participants using two methods: traditional approach versus technology-based approach. The technology-based approach recruited participants through various relevant Facebook groups. 77 women were approached for participation through their expressed interest on study advertisement postings. Of the 77 women, 38 were eligible (49.4%) and 28 consented and were recruited (73.7%). It was found that the most common reason for ineligibility in the technology-based group was that participants did not meet the minimum age requirement (n=17, 57%). This finding may suggest that South Asian women younger than 45 years old are using Facebook platforms more frequently than women older than 55 years old. Therefore, it may be more effective for recruiters to use a technology-based approach when studying South Asian women populations younger than 45 years of age. Alternatively, in the traditional method, 123 individuals were approached through word-of-mouth. Among the 123 women approached, 33 were eligible (26.8%) and 18 consented and were recruited (54.5%). The eligibility rate, recruitment rate, and recruitment screened ratio were higher for the technology-based approach than the traditional approach. This finding suggests that in addition to the traditional method, a technology-based approach may also be an effective and efficient approach for recruiting participants from the South Asian community.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,221 | 0,282 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,010 | 0,007 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».