Knowledge and attitudes of parents towards children with fever quantitative study
Bibliographic record
Abstract
Introduction: In 2018, the Direção-Geral da Saúde published a standard (Standard 014/2018) entitled Integrated Care Process for Short-Term Fever in Pediatric Age, with the purpose of standardizing the care provided by health professionals and instructing parents about measures to be adopted in the different phases of fever: temperature rise, plateau and defervescence. Objective: This study aims to compare the knowledge and attitudes of parents towards children with fever with the indications set out in the stated standard and to disseminate its guidelines. Methodology: Quantitative, observational and cross-sectional study in a non-probabilistic sample composed of 201 parents of children aged 2 to 10 years old. A questionnaire was prepared and submitted for pre-testing with 11 mothers, with no changes being suggested. An explanatory pamphlet, created by the author, was given to participants after completing the questionnaire. Data collection occurred in January/February 2024, in arbitrarily selected shopping malls, after authorization from the institutional Ethics Committee. Informed consent signed by participants and data confidentiality guaranteed. These were anonymized and processed using SPSS software, version 26. Results: sample composed mainly of participants living in urban areas (61.2%), aged between 24-52 years (M=37.81; SD=5.98), with a degree (34.4%) and professions not related to health (82.6%). They consider fever values ≥ 37.5ºC (45.3%), with warning signs being lack of appetite (53.7%) and excessive sleep (46.3%). Adequate measures were used by only 1.5% of the sample in thermal ascent, by 26.9% in situations of discomfort and by 34.3% in thermal descent. Regarding the use of antipyretics, only 32.3% resort to paracetamol monotherapy. Discussion: Comparing the Direção-Geral da Saúde indications contained in Standard 014/2018 with the knowledge and attitudes of most participants in this sample, they do not appear to proceed in accordance with the indications contained in the standard under consideration, which agrees with other studies found in the literature. Conclusions: Given the data presented, we found that the Direção-Geral da Saúde indications published around seven years ago still do not appear to be known by most participants. It is suggested that healthcare professionals reinforce teaching on this topic, promoting health literacy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".