Work Performance Analysis on the Implementer of the Adolescent Health Services Program at Health Center in District of Demak Year 2007
Bibliographic record
Abstract
Nowadays, adolescent health services are provided more attention by all people in the world due to increasing the number of adolescents which reach a quarter of world population and increasing the naughtiness cases on adolescent group. Health facilities and human resources at basic level of services have not provided the services for adolescent group in which this condition accounts for low coverage of adolescent health services at health centers. This fact shows that implementers of the adolescents health services program have not worked optimally. \nAim of this research was to find out implementer’s work performance of the adolescents health services program at Health Center in Demak District. Number of informants was seven persons who were the implementers of the program in Demak District. Method of triangulation was performed by collecting data from Head of Health Center (3 persons), adolescents (3 persons), and Head of Family Health Section (1 persons). \nData were analyzed using deductive-inductive method. Data of implementers’ work performance of the program and variables that influence work performance like ability, experience, attittude, motivation, sources, leadership, and reward were collected using descriptive-qualitative method. \n \nSumber Utama : www.mikm.undip.ac.id
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".