The Role of Paying for Access in the Online Information Seeking‐Behavior of Canadian Midwives: Preliminary Findings
Bibliographic record
Abstract
ABSTRACT It is of paramount importance to support midwifery practice with research evidence. Nevertheless, Canadian midwives encounter significant obstacles in accessing research evidence for practice. As part of a broader study which seeks to identify an optimal method for providing midwives in Canada with access to online clinical resources, this poster reports on the preliminary findings of the survey phase. The objective of this study was to ascertain the frequency with which Canadian midwives utilize information sources accessible to them through institutional affiliations and to determine whether they are aware of the financial cost of providing such services. Additionally, the survey sought to ascertain whether their perception of a resource being free, or premium affects its perceived value and use intentions. A descriptive survey design employing a structured web‐based questionnaire was used to collect the requisite data. The preliminary findings indicated that participants perceived the value of free sources to be lower than premium sources and that they intended to use premium sources more frequently if they were able to pay for them. Moreover, the study demonstrated that participants frequently seek information online to support clinical practice, yet they utilize access to online resources through their institutional affiliation less so.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".