Implementation Barriers: A TASKS Framework
Bibliographic record
Abstract
Implementation is ubiquitous. The identification of barriers to implementation is critical for achieving implementation success. This paper introduces and discusses a deductive theory-based framework, TASKS, to guide the identification of implementation barriers. The TASKS framework deals with the relationships between a Task and the task implementer’s Affect, Skills, and Knowledge, based on the inversed U-shaped mental Stress-mental effort relation. The TASKS framework classifies implementation barriers into four categories: 1) emotion barriers, 2) logic barriers, 3) knowledge barriers, and 4) resources barriers. The TASKS framework detects barriers to implementation following three steps, 1) identifying the ideal TASKS components, 2) modelling the implementer's mental capability, and 3) detecting barriers to implementation. The TASKS framework can be applied to a wide range of disciplines for effective and efficient task implementation.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".