Effectiveness of aromatherapy on physiological and psychological complaints during pregnancy: a systematic review protocol
Bibliographic record
Abstract
Objective: The objective of this review is to synthesize the available evidence to investigate the effectiveness of aromatherapy in the treatment of physiological and psycological complaints during pregnancy. Introduction: During pregnancy, women report common complaints that result from anatomical and physiological changes important for the development and protection of the maternal-fetal binomial. Aromatherapy is an complementary and integrative practice of ancient origin that works by administering essential oils to promote physical and psychological well-being. Inclusion criteria: This review will include studies of pregnant women with low risk of complications in the context prior to labor, who received aromatherapy as a treatment for their physiological and psychological complaints, common in pregnancy. Randomized clinical trials, quasi-experimental studies, cohorts and case-control will be included. Methods: Databases to be searched will include: PubMed, Scopus, CINAHL, Web of Science, CENTRAL, PsycINFO, LILACS, BDENF, CUIDEN and MOSAICO in Portuguese, English and Spanish, with no time limit. Unpublished literature will be searched in: ProQuest, Brazilian Digital Library of Theses and Dissertations, British Library EThOS and Canadian Theses and Dissertation Portal. The JBI approach will be used for study selection, critical appraisal, data extraction and data synthesis.
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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 | 0.059 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".