I-deals on the retention of human talent in health networks in Satipo, Peru
Bibliographic record
Abstract
The objective of the research was to determine if there is an influence of i-deals on the retention of human talent in the health networks of Satipo, Peru. The study was carried out from a quantitative approach, with a correlational-non-experimental-translational design. A questionnaire was administered to 308 workers who negotiated one or more idiosyncratic agreements between December 2023 and February 2024. Using the structural equation model, it was obtained that the i-deals of work content (i-TC) and development (i-D) have a higher level of influence on the retention of human talent (p <0.05), generating a high commitment to the organization; based on the appropriate job assignment to employees according to technical and soft skills, and aligned with their personal interests. On the other hand, flexibility (i-F) and financial (i-FI) i-deals do not influence the retention of human talent, because the remuneration is not aligned with the required professional skills. It is concluded that for the public health sector, employees prefer to have autonomy in the way they perform their work, opportunities for professional specialization rather than financial incentives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".