Fighting from the fringes : the decision-making process of mothers using complementary and alternative medicine in autism.
Bibliographic record
Abstract
In this study a theoretical model “Fighting from the Fringes” was developed to help elucidate the decision-making process of CAM selection by parents of children with autism. Represented in this conceptual model are the processes of searching for answers, looking for alternatives and making choices. The mothers’ disenfranchisement and marginalization from conventional healthcare, stigmatization from society and the stress and isolation experienced, all prove pivotal to the decision-making process of CAM use in autism. By examining the narratives of the mothers who participated in the study, and utilizing a grounded theory approach to analysis the data, this theoretical model emerged. This model allows for an understanding of the thought processes behind mothers’ decisions to use CAM for their children with autism, what also emerges is an understanding of how the use of CAM allows them to maintain their self-identify as “good mothers”. Recommendations of the study offer suggestions that address the much needed support and education to families face with caring for and making treatment decisions for their autistic child.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".