Free Access Does Not Necessarily Encourage Practitioners to Use Online Evidence Based Information Tools
Bibliographic record
Abstract
Objectives – To determine which strategies were most effective for encouraging general practitioners (GPs) to sign up for free access to an online evidence based information resource; and to determine whether those who accepted the offer differed in their sociodemographic characteristics from those who did not. Design – Descriptive marketing research study. Setting – Australia’s public healthcare system. Subjects – 14,000 general practitioners (GPs) from all regions of Australia. Methods – Subjects were randomly selected by Medicare Australia from its list of GPs that bill it for services. Medicare Australia had 18,262 doctors it deemed eligible; 14,000 of these were selected for a stratified random sample. Subjects were randomized to one of 7 groups of 2,000 each. Each group received a different letter offering two years of free access to BMJ Clinical Evidence, an evidence based online information tool. Randomization was done electronically, and the seven groups were stratified by age group, gender, and location. The interventions given to each group differed as follows: • Group 1: Received a letter offering 2 years of free access, with no further demands on the recipient. Evidence Based Library and Information Practice 2010, 5.4 84 • Group 2: Received a letter offering 2 years of free access, but on the condition that they complete an initial questionnaire and another one at 12 months, as well as allowing the publisher to provide de-personalized usage data to the researchers. • Group 3: Same as Group 2, but with the additional offer of an online tutorial to assist them with using the resource. • Group 4: Same as Group 2, but with an additional pamphlet with positive testimonials about the resource from Australian medical opinion leaders. • Group 5: Same as Group 2, but with an additional offer of professional development credits towards their required annual totals. • Group 6: Same as Group 2, but with an additional offer to be entered to win a prize of $500 towards registration at a conference of the winner’s choice. • Group 7: A combination of the above interventions. The group received the opinion leaders’ pamphlet, the online tutorial, and eligibility for professional development points. The online survey and usage data from Groups 2 through 7 was to be analyzed as part of a companion study, and is not reported in this article. To protect the privacy of individual subjects, Medicare Australia mailed out the offers and provided the authors with anonymized data, in table format, on response status by intervention group and by the following sociodemographic variables: age, gender, geographic remoteness as determined by the Accessibility/Remoteness Index of Australia (ARIA), country of graduation, and years since graduation. Baseline characteristics were compared between the intervention groups, and then response rates were also compared between intervention groups and between the above-mentioned variables to see whether any of these variables affected the likelihood of practitioners being interested in an online evidence based tool. All comparisons were done using a chi-square test. Main Results – Overall, 2,105 subjects returned their acceptance forms, out of the total sample of 14,000 (15%). The true acceptance rate was 12.5%, however, when adjusted for the number of subjects in Groups 2 through 7 who went on to complete the online questionnaire. There was a statistically significant difference in response rates between the seven groups, with the greatest acceptance rate (27%) coming from Group 1 (who received only the letter of offer, with no experimental demands). The other groups averaged a response rate of 10% collectively, with the lowest rates (8.0% and 8.5% respectively) from Group 5 (offer of professional development points) and Group 7 (combination of interventions). The large sample size offered adequate power to detect differences in characteristics between responders and non-responders. The study found that responders were more likely to be younger, male, recent graduates, and practising in less remote locations. Among responders, there were no statistically significant differences in most of these characteristics among the seven groups, with the exception of time since graduation, which varied somewhat. Conclusion – The authors conclude that funding of access to free online resources for large groups of practitioners may not be cost-effective if calculations of cost are based on total eligible populations rather than on the number of practitioners who may be interested. They also conclude that the low response rates generated by their offer indicate a need to find ways to increase GPs’ interest in using online evidence based tools and in accessing best practice evidence. Further research into how to achieve behaviour change among practitioners may be needed.
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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 | 0.031 | 0.185 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".