The Impact of mHealth Interventions on Breast Cancer Awareness and Screening: Systematic Review Protocol
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) is the use of mobile communication technologies to promote health by supporting health care practices (eg, health data collection, delivery of health care information). mHealth technologies (such as mobile phones) can be used effectively by health care practitioners in the distribution of health information and have the potential to improve access to and quality of health care, as well as reduce the cost of health services. Current literature shows limited scientific evidence related to the benefits of mHealth interventions for breast cancer, which is the leading cause of cancer deaths in women worldwide and contributes a large proportion of all cancer deaths, especially in developing countries. Women, especially in low- and middle-income countries (LMICs), are faced with low odds of surviving breast cancer. This finding is likely due to multiple factors related to health systems: low priority of women's health and cancer on national health agendas; lack of awareness that breast cancer can be effectively treated if detected early; and societal, cultural, and religious factors that are prevalent in LMICs. The proposed systematic review will examine the impact of mHealth interventions on breast cancer awareness and screening among women aged 18 years and older. OBJECTIVE: The objectives of this study are to identify and describe the various mHealth intervention strategies that are used for breast cancer, and assess the impact of mHealth strategies on breast cancer awareness and screening. METHODS: Literature from various databases such as MEDLINE, EMBASE, PsycINFO, CINAHL, and Cochrane Central Register of Controlled Trials will be examined. Trial registers, reports, and unpublished theses will also be included. All mobile technologies such as cell phones, personal digital assistants, and tablets that have short message service, multimedia message service, video, and audio capabilities will be included. mHealth is the primary intervention. The search strategy will include keywords such as "mHealth," "breast cancer," "awareness," and "screening," among other medical subject heading terms. Articles published from January 1, 1964 to December 31, 2016 will be eligible for inclusion. Two authors will independently screen and select studies, extract data, and assess the risk of bias, with discrepancies resolved by dialogue involving a third author. We will assess statistical heterogeneity by examining the types of participants, interventions, study designs, and outcomes in each study, and pool studies judged to be statistically homogeneous. In the assessment of heterogeneity, a sensitivity analysis will be considered to explore statistical heterogeneity. Statistical heterogeneity will be investigated using the Chi-square test of homogeneity on Cochrane's Q statistic and quantified using the I-squared statistic. RESULTS: The search strategy will be refined with the assistance of an information specialist from November 1, 2017 to January 31, 2018. Literature searches will take place from February 2018 to April 2018. Data extraction and capturing in Review Manager (RevMan, Version 5.3) will take place from May 1, 2018 to July 31, 2018. The final stages will include analyses and writing, which is anticipated occur between August 2018 and October 2018. CONCLUSIONS: The knowledge derived from this study will inform health care stakeholders, including researchers, policy makers, investors, health professionals, technologists, and engineers, on the impact of mHealth interventions on breast cancer screening and awareness. TRIAL REGISTRATION: Prospero registration number CRD42016050202.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".