Graduate certificate in local development planning, land use management and disaster risk management: a knowledge, attitude and practice (KAP) evaluation
Bibliographic record
Abstract
Purpose This study aims to assess knowledge retention of the graduates of the online graduate certificate on local development planning, land use management and disaster risk management (PDLOTGR, the abbreviation of the certificate's Spanish title). The certificate was offered to practitioners and faculty members of Latin American countries since 2016. Design/methodology/approach The authors reviewed the knowledge, attitude and practice (KAP) method to develop a specific approach, which included the preparation of a KAP survey, a composite KAP index and three sub-indices. The survey targeted two groups: (1) experimental group, composed of the certificate's 76 graduates, and (2) control group, comprised of 25 certificate's candidates, who had not yet undergone the training/intervention. The statistical analysis included a one-way multivariate analysis of variance to compare the mean scores on the KAP index and sub-indices for individuals in the experimental and control groups. Findings The study results showed significant differences in the knowledge sub-index between those who had completed the PDLOTGR training and those who had not, while the attitudes and practices sub-indices did not show significant differences. When using the KAP index, a statistically significant difference was also observed between the two groups. Originality/value Perceived knowledge assessment offers an acceptable and non-intimidating option for evaluating continuing education and professional development programs associated to disaster risk. It is particularly helpful in determining whether an intervention or program has a lasting impact. It is not, however, a substitute for direct knowledge assessment, and the use of other methods to evaluate the performance of a capacity building program's graduates.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".