Risk Assessment in Developing Occupational Standards for Environmental Work in Thailand
Bibliographic record
Abstract
This qualitative research aimed to conduct a risk assessment for developing occupational standards for environmental work in Thailand by collecting data through semi-structured interviews from 49 key informants using purposive sampling from consultants, working groups, and the endorsement board.Content analysis was applied by selecting content pertinent to the research question, defining coding categories, coding the content, and analyzing the results in relation to the keywords and risk assessment.The results were interpreted according to: 1) Publicizing the project (low and moderate risks); 2) Studying role model countries (moderate risk); 3) Conducting functional analysis (moderate risk); 4) Making and testing assessment tools (low risk); and 5) Proposing to the endorsement board (moderate risk).The new processes were proposed as: 1) Determining the conditions of selection target group (government agencies, private sector, and independent); 2) Determining the public relations and period; 3) Training on Functional Analysis; 4) Recruiting the working group; 5) Determining common and specific competency; 6) Determining the ratio of academic and practical tool; 7) Meeting for understanding and self-assessment of the testing groups and examiners; 8) Compiling the list of professional experts; 9) Collecting feedback for improvement; and 10) Creating benefits perception of the occupational standard.
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 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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".