A model for collaborative evaluation of university-community partnerships
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Manitoba's The Need to Know project was presented with a unique opportunity to develop a collaborative approach to evaluation, and to explore the effectiveness of a variety of evaluation methods for assessment of university-community collaborative health research partnerships. OBJECTIVES: The evaluation was designed to incorporate participation of community partners in planning, developing, and evaluating all aspects of the project. Objectives included: (a) assessment of extent to which the project met its initial objectives; (b) assessment of extent participants needs and expectations were met; (c) refinement of evaluation questions; (d) identification of unanticipated impacts; (e) assessment of participant confidence as research team members; (f) development of knowledge translation theory; and (g) component analysis. METHODS: A "utilisation focused" approach was used. Primary stakeholders identified evaluation questions of concern, and how findings would be used. The multimethod time series design incorporated key informant interviews, a pre/post-test survey, written workshop evaluations, and participant and unobtrusive observation. All aspects of the evaluation were made transparent to participants, and formal feedback processes were instituted. RESULTS: There was a high level of participation in evaluation activities. Identifying evaluation questions of concern to community partners helped shape project development. While all methods provided useful information, only key informant interviews, participant observation and feedback processes provided insights into all evaluation objectives. CONCLUSION: Collaborative evaluation can make an important contribution to development of university-community partnerships. Qualitative methods (particularly key informant interviews, participant observation, and feedback processes) provided the richest source of data, and made an important contribution to team development.
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.
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 | 0.138 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it