Mobile health app engagement and counselling uptake in fertility patients: a preliminary study
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".