Examining Multilevel Factors Associated with the Process of Resilience among Women Living with HIV in a Large Canadian Cohort Study: A Structural Equation Modeling Approach
Bibliographic record
Abstract
OBJECTIVES: We examined how multiple, nested, and interacting systems impact the protective process of resilience for women living with HIV (WLWH). METHODS: Using data from a Cohort Study, we conducted univariate analyses, multivariable logistic regression, and a 2-step structural equation modeling for the outcome, high resilience (N = 1422). RESULTS: Participants reported high overall resilience scores with a mean of 62.2 (standard deviation = 8.1) and median of 64 (interquartile range = 59-69). The odds of having high resilience were greater for those residing in Quebec compared to Ontario (adjusted odds ratio [aOR] = 2.1 [1.6, 2.9]) and British Columbia (aOR = 1.8 [1.3, 2.5]). Transgender women had increased odds of high resilience than cisgender women (aOR = 1.9 [1.0, 3.6]). There were higher odds of resilience for those without mental health diagnoses (aOR = 2.4 [1.9, 3.0]), non-binge drinkers (aOR=1.5 [1.1, 2.1]), and not currently versus previously injecting drugs (aOR = 3.6 [2.1, 5.9]). Structural equation modeling confirmed that factors influencing resilience lie at multiple levels: micro, meso, exo, and macro systems of influence. CONCLUSION: There is a need to consider resilience as the interaction between the person and their environments, informing the development of multilevel interventions to support resilience among WLWH.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".