Humanizing practices developed in response to the demands of the sobral family health strategy
Bibliographic record
Abstract
Humanization in healthcare is not a recent agenda. In the Brazilian Unified Health System (SUS), the discussion about a humanized care gave birth to the National Humanization Policy (PNH), back in 2003, when significant advances in our public health system competed with a wide range of problems. PNH valorizes innovative attitudes considering dialogue between patients, professionals and health managers. Nonetheless, its operationalization is still challenging. In this study, our objective was to know how National Humanization Policy has been performed in Family Health Strategy in Sobral – CE, as well as to understand the knowledge of professionals about this politics, identifying which humanized practices are performed as well as what difficulties are found concerning humanization. For our purposes, we developed an exploratory qualitative study. The scenario consisted of the urban Family Health Centers (CSFs) of Sobral, and the target public was composed by nurses and community health agents. Data collection occurred in September and octuber, 2018. Data were collected using a semi-structured interview along with observation and field diary, with posterior Content Analysis. Results showed that the Health Centers have weak structures that challenge assistance confidentiality and do not allow group activities; however, the centers allow patients access and contribute to people‟s well-being. Regarding PNH text, most of the professionals do not know it. The practices performed in the CSFs that are clearly related to the PNH are: Reception, Group Activities, the Quarter Circle, and the Local Health Councils. The main defying difficulties laid involved weak sense of teamwork, flow disruption and the absence of joint responsibility by users. We concluded that the Family Health Strategy in Sobral has actions that favor humanization of health care, but the National Humanization Policy still needs to be implemented effectively, so that the challenges can be overwhelmed and the team work can be reinforced, along with social bonds and joint responsibility.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".