Using an Online Measurement Data Management Platform to Improve Survey Response Rates in a Community Sexual Assault Centre
Bibliographic record
Abstract
PURPOSE: Data collection in community organizations can be challenging, but important for evaluative initiatives as well as for therapeutic purposes, such as for organizations engaged in measurement-based care. This study tested the impact of an online measurement data management platform (OMDMP) on mental health assessment response rates at a community-based sexual assault crisis center. We examined whether implementing the OMDMP improved client assessment participation in mental health assessment questionnaires compared to manual data collection methods. Materials and Methods: Using a pre-post design, we analyzed data from two time periods: pre-pandemic (manual assessments) and mid-pandemic (OMDMP assessments). Data included clients' mental health assessments, using standardized tools such as the DASS-21 and IES-R. RESULTS: Our analysis revealed a significant increase in the rate of clients completing at least one mental health assessment, rising from 45% (manual) to 71% after the introduction of the OMDMP. We also found that the OMDMP prompted clients to complete their assessments generally on time, contributing to effective use of assessments as a component of measurement-based care. DISCUSSION: Although this improvement demonstrates clear benefits for data collection in a community setting, challenges remain in ensuring both pre- and posttest completion. While online tools can enhance organizational capacity for self-evaluation and improve client participation in measurement-based care, attention is still needed to address gaps in the data collection process. CONCLUSION: We discuss the successes and barriers encountered during the implementation of the OMDMP and its potential implications for social work practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".