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Record W2941603904 · doi:10.5430/wje.v9n2p109

Scoping Review of the Core Elements of Technical Assistance Models and Frameworks

2019· article· en· W2941603904 on OpenAlexvenueno aff
Carl J. Dunst, Kimberly Annas, Helen Wilkie, Deborah W. Hamby

Bibliographic record

VenueWorld Journal of Education · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCore (optical fiber)Process managementComputer scienceBest practiceManagement scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.159
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.056
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.159
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0440.046
Science and technology studies0.0030.003
Scholarly communication0.0090.009
Open science0.0050.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.071
GPT teacher head0.353
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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