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Record W3064026061 · doi:10.1093/pch/pxaa068.056

57 The family experience of the Autism Spectrum Disorder diagnostic conference: A qualitative meta-synthesis

2020· article· en· W3064026061 on OpenAlexaboutno aff
Alexandra Jackman, Kassi A. Boyd, Lisa Tjosvold, Lonnie Zwaigenbaum, Shanon Phelan

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchConversationAutism spectrum disorderMedical diagnosisAutismSystematic reviewPsychologyMEDLINEMedicineFamily medicinePsychiatrySociologyPathologySocial science

Abstract

fetched live from OpenAlex

Abstract Background The Canadian prevalence of Autism Spectrum Disorder (ASD) is one in 66 children affecting many families nationwide. Increasingly, clinicians are tasked with discussing new ASD diagnoses with families; however, many physicians are not comfortable with the conversation, despite self-reported familiarity with ASD. Concurrently, research indicates that families are often dissatisfied with their diagnostic journey, including the diagnostic conversation, which has been described as “profound to almost all parents” (Abbott et al., 2012). Given the importance of this moment, we applied a qualitative meta-synthesis design to gain a deeper understanding of the family experience. Meta-synthesis is an emerging field in health sciences, wherein a systematic search strategy is coupled with qualitative analysis. It is valuable for evidence-driven practices and policies as large volumes of qualitative literature are synthesized into actionable concepts. Objectives We aim to describe and appreciate the family experience of an ASD diagnostic conference. We define the diagnostic conference as is the meeting where children, parents, and/or families are told that the child has an ASD diagnosis. Design/Methods We conducted a systematic search to capture relevant qualitative studies, including all qualitative approaches and qualitative components of mixed-methods studies. A search strategy was developed by a medical librarian with systematic review expertise. An initial search of three databases was undertaken to identify keywords. These terms were then used in searching a wider array of pertinent databases. The search was not limited by dates. Applying Saini and Shlonsky’s (2012) meta-synthesis method, included studies’ demographic and contextual data will be extracted. “Findings/Results” sections of included articles will also be extracted and coded by two independent reviewers. Codes will be translated into themes by an interdisciplinary team of two to five reviewers applying an inductive and iterative process, with a critical disability theoretical lens. Themes will be integrated to form an overall synthesis of the family experience of the ASD diagnostic conference. Results In total, 1329 titles/abstracts were reviewed: 23 were selected for inclusion and 24 are pending team discussion. Preliminary analysis reflects shared concepts among included articles such as: provider-family rapport; conflict over who is the expert; comprehensiveness; language; body language; individuals present; physical space; elements of ASD emphasized (positive vs. negative); hope. Most studies were based in North America or Europe with Caucasian participants represented. Mothers were relatively over-represented as participants. In applying a critical disability theoretical lens, initial observations of the language of included studies present a negative framework for meaning making of an ASD diagnosis. Conclusion This meta-synthesis will provide an in-depth appreciation of the family experience of the ASD diagnostic conference and explore the context of published research. In doing so, it may inform individual clinician practices, medical education around communication, and family-centered-care policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.076
metaresearch head score (Gemma)0.196
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.076
Threshold uncertainty score0.402

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.196
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0180.014
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.121
GPT teacher head0.399
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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