Mobile health app engagement and counselling uptake in fertility patients: a preliminary study
Notice bibliographique
Résumé
Infertility affects up to one in six Canadian couples and is a challenging experience, associated with increased stress, depression, and decreased quality of life (Cousineau & Domar, 2007; Fisher & Hammarberg 2012; Greil et al., 2010), and some patients may benefit from accessing counselling services. However, the large difference between patients’ interest in counselling services and actual uptake (Laflont & Edelmann, 1994; Wischmann et al., 2009;) suggests that some patients may experience barriers to accessing mental health care. Common barriers outlined in the mental health help-seeking literature include a lack of information, especially about recognizing symptoms of mental illness and how to access care (Boivin et al., 1999; Dawadi et al., 2018; Mojtabai et al., 2011; Read et al., 2014), and attitudinal barriers, such as wanting to handle the problem by oneself, and stigma of mental health help-seeking (Clement et al., 2015; Gulliver et al., 2010). Accordingly, the provision of psychosocial information – information that addresses how infertility impacts several domains of a patient’s life (such as the couple and broader social relationships), the psychological distress it can cause, and information about coping strategies, including counselling –may be a feasible method of encouraging counselling uptake amongst those who want it. This thesis presents the results of a pre-post repeated measures study of Infotility, a mobile health app containing information relating to infertility and reproductive health, including psychosocial information. We recruited 166 male and female fertility patients from clinics in Montreal and Toronto. Specifically, we examined: (1) What independent variables (patient characteristics, fertility treatment-related, and psychological factors), were associated with greater engagement with the psychosocial app content; (2) Whether the independent variables and engagement with the psychosocial content were associated with counselling uptake post-intervention amongst the entire sample of study participants, and; (3) whether the independent variables and engagement with the psychosocial content were associated with counselling uptake amongst the sub-sample of participants with an unmet need for counselling –those who wanted, but did not seek, counselling pre-intervention. Results indicated that: (1) Having an unmet need for counselling was the only variable significantly associated with greater participant engagement with the psychosocial app content; (2) In the entire sample of participants, the receipt of mental health information from a healthcare provider and greater perceived stress were significantly associated with counselling uptake post-intervention, and; (3) Within the sub-sample of those who expressed an unmet need for counselling, receiving information from a healthcare provider was significantly associated with counselling uptake. Participants who demonstrated greater engagement with the psychosocial app content and those who earned over $100,000 per year were also more likely to seek counselling post-intervention.Our results suggest that information provision is a key factor in encouraging counselling uptake in fertility patients, and that healthcare providers play an important role in disseminating this information. Exploratory findings also speak to the potential of using a mobile health app to provide fertility patients with psychosocial information and encourage counselling uptake for those who want it. The primary clinical implication of this research is that to address fertility patients’ informational and psychological needs, health care providers should make efforts to provide all fertility patients with psychosocial information, which could be given in-person, or through mHealth patient education materials. Future research should investigate the utility of mHealth information provision for encouraging counselling uptake in a larger sample of fertility patients
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 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 | 0,007 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 ».