Perceived Community Stigma of Mothers Who Use Substances (SMUS): Initial Scale Development
Bibliographic record
Abstract
Background: Mothers who misuse substances encounter stigma which can lead them to avoid seeking addiction treatment; however, there are no tools that assess their perceptions of such stigma. We therefore aimed to provide the initial development and tests for internal consistency and construct validity of a tool that measures perceived community stigma against mothers who use drugs (SMUS).Methods: The research took place from October to December 2023 in Canada. We used a mixed methods approach to develop the scale, which involved two steps. Step 1 consisted of item generation, using a qualitative and inductive approach. Step 2 involved initial psychometric analysis, using quantitative data to assess internal consistency and construct validity. Step 1 (n=27) included women who were in recovery and treatment providers, and Step 2 (n=82) included women in recovery. Thematic results from Step 1 were used to generate 14 items for Step 2.Results: In Step 1, five themes emerged regarding stigma experienced by mothers who use substances, including child services, childcare, needs of children, irresponsible and selfish, and gossip. In Step 2, the Cronbach's alpha score for the final scale items was 0.86. SMUS was also positively correlated with enacted stigma (tau= 0.15, p= 0.05) and internalized stigma (tau= 0.26, p= 0.002), indicating convergent validity of SMUS.Conclusions: This research adds value to extant research on stigma and substance misuse by developing and providing results for internal consistency and construct validity of a scale that measures perceptions of community stigma against mothers who use substances.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".