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Surplus suffering: the search for help when a child has mental‐health issues

2012· article· en· W1874087877 on OpenAlexaff
Juanne N. Clarke

Bibliographic record

VenueChild & Family Social Work · 2012
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMedicalizationMental healthPsychologyRelevance (law)Context (archaeology)PsychiatryAnxietyQualitative researchAutismDevelopmental psychologySociologySocial science

Abstract

fetched live from OpenAlex

ABSTRACT Social theorists have demonstrated the growth in dominance of two central discourses for understanding the ways that children's mental‐health issues are understood today – medicalization and intensive mothering . In this context, this paper reports on a qualitative interview‐based study of 16 mothers whose children had received a diagnosis with one or more mental‐health or developmental issues such as Tourette's, bipolar, anxiety, depression, autism or attention deficit hyperactivity disorder. It is based on the retrospective accounts of mothers given during interviews, from the time when they noticed what they thought to be unusual behaviours and decided to try to normalize and accommodate to their children's behaviours and then to the various steps they took to seek help. The paper begins with a description of the sorts of problems that mothers noticed. It then moves to the strategies mothers then took to cope, manage and socialize their children. When these failed, mothers sought professional assistance with understanding, remediation and/or a diagnosis for the child(ren). Mothers described uncertainty, confusion and contradictions as they unremittingly sought help. This process may be called surplus suffering . The relevance of the theoretical issues is then reconsidered along with the substantive and practical consequences of the findings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.334
Teacher spread0.266 · 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 designNot applicable
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

Citations6
Published2012
Admission routes1
Has abstractyes

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