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 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 | 0.112 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".