ArticleFamilial cancer2019
Exploring the preferences of involved health professionals regarding the implementation of an online decision aid to support couples during reproductive decision-making in hereditary cancer: a mixed methods approach.
Article in Familial cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Factors influencing fertility preservation communication and decision support for cancer patients: a qualitative evidence synthesis of healthcare professionals' perspectives.Frontiers in endocrinology · 2026Review
- The effects of an online decision aid to support the reproductive decision-making process of genetically at risk couples-A pilot study.Journal of genetic counseling · 2023Article
- Implementing a patient decision aid, a process evaluation of a large-scale pre- and post-implementation trial.Breast cancer research and treatment · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To support persons having a genetic predisposition to cancer and their partners during reproductive decision-making, an online decision aid was developed and evaluated. To maximize the impact of the support tool, this mixed methods study aims at developing the optimal implementation strategy for the decision aid. A questionnaire to assess the critical determinants that may affect this implementation was completed by health professionals involved in oncogenetic counselling (N = 46). Subsequently, semi-structured focus groups (N = 19) and individual telephonic interviews (N = 15) were performed with a subset of health professionals. All health professionals indicated to be willing to refer couples to the decision aid, preferably at the moment of receiving the genetic test result. They agreed that the primary requirement for implementation in daily practice was ease of referring couples and preferably free online accessibility. Referral to the tool was able to be included in the standard report couples receive after consultation, thereby making the use of additional paper-based materials redundant (e.g. flyers). Furthermore, incorporating the link to the decision aid on patient organization websites was suggested. Health professionals agreed that implementation would benefit more from promoting awareness regarding the decision aid rather than the inclusion of the tool in official clinical guidelines. To foster implementation of the decision aid, the distribution of online newsletters and the designation of a contact person charged with continued implementation in each Clinical Genetic Center were suggested. Based on these preferences and recommendations, the implementation of the online decision aid will be nationally executed to optimize impact.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.