Conducting Evaluation Research with Hard-to-Follow Populations: Adopting a Participant-Centred Approach to Maximize Participant Retention
Bibliographic record
Abstract
Abstract: Longitudinal designs are effective for the evaluation of innovative social programs, but attrition can be a significant problem, particularly with hard-to-follow populations such as persons who are homeless. Tracking strategies for locating participants are essential, but retaining participants requires anticipating and addressing participants’ needs at every stage of the research. A strategy that emphasizes appropriate interviewer characteristics, the relationship between interviewer and participant, and participants’ “investment” in the research is critical. In other words, evaluation researchers can improve the retention of even hard-to-follow study participants by adapting research design and procedures to be “participant-centred.” An example is given of a program evaluation in Ottawa, Ontario, that implemented strategies to adapt to the needs of persons with severe mental illness and a history of homelessness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| grok | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: yes | Other design | high |
| opus | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.569 | 0.475 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".