Surplus suffering: the search for help when a child has mental‐health issues
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".