Social support among gender diverse people: Are we measuring what we think we are?
Bibliographic record
Abstract
Background: To assess social support among gender diverse people, measures normed and validated with cisgender participants are used, most commonly the Multidimensional Scale of Social Support (MSPSS). Despite widespread use, the psychometric properties of the MSPSS have not been systematically investigated among gender diverse people. Thus, it is unclear whether use of the MSPSS is appropriate for this population. Method: = 428). The study was conducted in Canada between 2019 and 2020. Results: After running confirmatory factor analyses on MSPSS items, the factor structure differed from prior studies of cisgender participants. First, support from a significant other did not correlate with support from family or friends, suggesting its inclusion in a global measure of social support may distort findings when all domains are combined. Second although social support from family and friends emerged as two separate domains, qualitative data suggested that the boundaries between these forms of support were unclear. A deductive thematic analysis further highlighted properties of the scale that may not align with gender diverse peoples' experiences. Conclusions: Overall, these findings raise questions about the interpretation of the MSPSS when used with gender diverse people and suggest that an adapted or newly developed measure is needed.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".