Theoretical Perspectives of Occupational Health Nurses (OHN) career in Indonesia
Bibliographic record
Abstract
Background: According to the Central Statistics Agency (BPS, 2020), the number of medium-large industries in 2019 in Indonesia 30,381. This number is followed by the growth in healthcare services, which is classified as the highest among 17 existing sectors (Databoks, 2020). These opportunities and challenges prove that from the OHN employment perspective, the prospects for the OHN professionals are very promising. In terms of education where nursing specialization in Indonesia is still very limited, the career of the OHN corporate nursing profession needs to be analyzed and explored.Purposes: This review aims to enhance the understanding of OH nurses' careers in an increasingly dynamic educational environment, provides comprehensive understanding of the world of work for OHN nurses, and offers future research direction.Method: The methods used was to review three career model Career Framework, Change Model by Kotter and comparing with the competency-based by Delphi Model and Quinn Model. The results of the analysis and exploration were applied with a review of the nursing education system by the Ministry of Health, the National Education System, Ministry of Higher Education (Ristekdikti), Development and Empowerment of Healthcare Human Recources (BPPSDM), OHN Career Guide Canada and several journals of the last five years, from 2016 to 2020.Result: The review shows there is significant relationship between nursing education career paths in Indonesia and the flow and structure recommended by the three models in general nursing career, not specifically in the OHN perspectives.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".