Examining the mental health of siblings of children with a mental disorder: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Mental disorders affect 1 in 5 children having consequences for both the child and their family. Indeed, the siblings of these children are not insulated from these consequences and may experience elevated levels of psychological distress, placing them at increased risk for developing mental disorders. This protocol describes the methodology for a scoping review that will examine how mental disorders in children impact the mental health of their sibling(s). Further, we aim to examine the role of sex, gender, birth order, age of each child, and familial factors (e.g., parent mental illness, family structure), in sibling mental health. The proposed review will also identify resources that aim to support the needs of siblings of children with mental disorders. Taken together, this proposed review aims to take a fundamental step towards determining intervention targets to reduce the transmission of risk between siblings. AIM: The proposed scoping review aims to address the following questions: i) how do mental disorders (in children <18 years of age) impact the mental health of their sibling(s) (also <18 years of age)? ii) Can we identify resources designed to address the needs of siblings of children with mental disorders? METHODS: We will conduct the proposed scoping review in keeping with the six-stage Arksey and O'Malley Framework and the scoping review methodology provided by the Joanna Briggs Institute. In section i) we outline our research questions. In section ii) we describe our process for identifying studies that examine the mental health of siblings of a child with a mental disorder and studies that provide evidence on resources directed specifically at these siblings. We will search peer-review and grey literature published between 2011 and 2022 from OVID MEDLINE, OVID EMBASE, CINAHL Complete, Proquest Nursing and Allied Health, PsycINFO (via APA platform), Proquest Sociology Collection and Web of Science Core Collection and Proquest Theses and Dissertations. Section iii) describes our process for selecting relevant studies. In sections iv and v, we describe our methods for charting and summarizing relevant data. Finally, in section vi) we describe our integrative knowledge translation plan that aims to include knowledge users in interpretating and translating evidence gathered from the proposed review.
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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 | 0.120 | 0.130 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".