THINGS TO CONSIDER WHEN SELECTING AN EXTERNAL EVALUATOR
Bibliographic record
Abstract
In the field of injury prevention (IP), program funders are increasingly calling upon the organizations that they fund to demonstrate that they are engaged in “evidence-based practice”. In order to provide this required demonstration, service provider organizations must engage in the evaluation of their programs and projects (Ontario Ministry of Health & Long Term Care, 2002). However, the vast majority of organizations that are involved in the field of injury prevention do not have the benefit of having staff members who possess expertise in program and project evaluation. Most organizations in this field are from the not-for-profit sector and their staff members are primarily concerned with program development, delivery and ongoing administration. Therefore, the majority of IP organizations will need to look outside of their organizations in order to obtain needed evaluation expertise. This paper presents a brief introduction on issues to consider when engaging external evaluation expertise. KEY ISSUES & STRATEGIES: IP practitioners who are seeking to bring evaluation expertise into their organizations on a time-limited basis are faced with a number of important issues and questions including the following: 1) How much and what type of internal resources are available to contribute to this evaluation (e.g., project supervision, direct involvement of line staff in the evaluation)? 2) What type of external evaluator would be best for the evaluation in question (e.g., university-based, individual consultant, major consulting firm)? 3) What style of evaluation practice would best suit the needs of their IP organization? 4) What requirements, if any, does the program sponsor have with regard to this specific evaluation? 5) How will the evaluation findings be utilized within the IP organization and elsewhere?
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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 | 0.228 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".