Scoping Review of the Core Elements of Technical Assistance Models and Frameworks
Bibliographic record
Abstract
A review of 25 technical assistance models and frameworks was conducted to identify the core elements of technicalassistance practices. The focus of analysis was on generally agreed upon technical assistance practices that wereconsidered essential for planning, implementing and evaluating the effectiveness of technical assistance. Resultsindicated that there are five major components of technical assistance and 25 different core elements. Analyses of themodels and components found considerable variability within and between components in terms of the core elementsthat are considered most important or essential. Findings were used to define and describe the core elements of thetechnical assistance models and frameworks and how they can be used in research and evaluation studies todetermine if the use of the core elements and practices are related to changes or improvements in program,organizational, or systems practices.
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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 | 0.056 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.044 | 0.046 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.004 |
| 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, 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".