Developing and validating an evidence-based health promotion program for reproductive donors reflecting their challenges in the process of donation decision-making: a multi-method study protocol
Bibliographic record
Abstract
BACKGROUND: The demand for reproductive donation has increased globally. Donors face various challenges, which make decision-making difficult for them and may impact their health and quality of life. It seems that there is no ongoing, holistic healthcare program for reproductive donors in the fertility clinics. To fill this knowledge gap, the current study explores Iranian reproductive donors' challenges in the process of donation decision-making to incorporate the findings in developing and validating a needs-based and evidence-based health promotion program. METHODS: This multi-method study will be conducted in three stages. Stage one will employ a qualitative design based on grounded theory methodology to conduct interviews with participants, including embryo, oocyte, and sperm donors, as well as other key informants such as gynecologists, urologists, midwives, psychologists, and donors' family members, across three cities in Iran. Using purposeful and theoretical sampling, data will be collected by face-to-face, in-depth semi-structured interviews and analyzed by the constant comparison method adopted by Corbin and Strauss. Stage two will focus on planning a health promotion program, incorporating qualitative data collected in stage one into the stages of program planning. The program planning will be based on the six steps of the Public Health Ontario model. In the third stage, the program will be validated by the nominal group technique. DISCUSSION: The theoretical framework generated at the first stage will demonstrate reproductive donors' concerns regarding donation decision-making, the context in which they make their decisions, the strategies they adopt to respond to their issues, and their consequences. It will demonstrate conditions that facilitate or hinder donors' decision-making. The findings of this stage will inform the development of a health promotion program, which will be evidence-based and tailored to the needs of reproductive donors and the sociocultural context in which they undergo the donation process. This program, subject to validation by experts, could be adopted by fertility centers to enhance reproductive donors' health and quality of life. The findings of this study can be used as a basis for further relevant research.
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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 | 0.123 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".