Information needs of people seeking fertility services in Canada: a mixed methods analysis
Bibliographic record
Abstract
Background Infertility is a challenging experience associated with high levels of psychological distress. Many people seeking fertility services use the internet to obtain information about their conditions and treatments.Objectives This mixed-methods study aimed to describe the information-seeking experience of people seeking fertility services with respect to the fulfillment of their individually defined information needs and explore relationships between the fulfillment of information needs and psychological outcomes.Methods One hundred and four participants completed a survey with close-ended and open-ended questions about their experience using an informational web-based application (app) called ‘Infotility’ and about their mental well-being before and after using the app. The questionnaires administered were the The Mobile Application Rating Scale (uMARS), the Fertility Quality of Life questionnaire (FertiQol), the Patient Empowerment Questionnaire (PEQ) and the General Anxiety Disorder 7-item Scale (GAD-7). Eleven participants completed in-depth qualitative interviews about their experience using the app. A thematic analysis was used to interpret qualitative results and quantitization was used to dichotomize participants into those with met information needs versus those with unmet information needs. Google Analytics was used to compare participants’ reported experience with their actual use of the app.Results The results of this study show that there is variability in the amount of information that people seeking fertility services wish to receive. Participants whose information needs were met reported improved psychological outcomes after using the app, while those with unmet needs showed no change in their psychological outcomes.Conclusions Our results suggest that fulfilling information needs was associated with improved psychological outcomes in people seeking fertility services. Our results also suggest that individual differences in information needs should be considered when developing health educational materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".