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Challenges and unmet needs of mothers of preschool children with autism spectrum disorders in Tunisia: a qualitative study

2022· article· en· W4311928376 on OpenAlexaff
Nihed Abid, Asma Ben Hassine, Naoufel Gaddour, Sihem Hmissa

Bibliographic record

VenuePan African Medical Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDenialMedicineAutism spectrum disorderQualitative researchContext (archaeology)Psychological interventionAutismDevelopmental psychologySet (abstract data type)Social supportPsychiatryClinical psychologyPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Introduction: Autism spectrum disorder (ASD) is a life-changing condition, not only for the child but also for the mother and the usual caregiver. In fact, a child recently diagnosed with ASD is a real challenge to mothers´ adaptation, involves their resources, and gives rise to a set of needs. This study explores the unmet needs and experiences of mothers of ASD children in the Tunisian context. Methods: a qualitative phenomenological design was chosen for this study and a semi-structured interview was used for eight mothers raising an autistic preschooler child. Results: the results indicate significant denial and rejection following the announcement of the diagnosis. To cope with this, reliance on religion has helped foster acceptance. Although informal support (from family and friends) has sometimes been mentioned, an increased need for training, social and financial support has been expressed and is a major concern given the high cost of TSA services. Conclusion: this study provides a deeper understanding of mothers' needs following the announcement of the diagnosis of ASD. These unmet needs should be taken into account when designing interventions strategies for children with ASD to help mothers cope and parent a child with ASD.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.359
Teacher spread0.327 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations8
Published2022
Admission routes1
Has abstractyes

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