Using an intervention mapping approach to develop and implement a social media intervention
Bibliographic record
Abstract
The virtual research environment called the CardiOthoracic Nurses and Allied Professionals rEsearch Network (CONNECT) was created to support, mentor and train these professionals. This paper aims to describe the creation of a social media intervention using an intervention mapping theoretical approach. Intervention Mapping Theory (IMT) was used for the design of the CONNECT social media intervention. IMT is a systematic approach to intervention design and consists of six steps. In the first stage of the IMT, a limited number of evidence produced by nurses was identified. In the second step, the objectives of the CONNECT were determined: increasing brand awareness, increasing user familiarity, association and promoting various opportunities offered. In the third and fourth step, the intervention was developed based on theory and empirical evidence to create the content for the posts and to determine the type of platform and frequency of use. In the fifth step, a protocol was created to guide the adaptation, implementation and maintenance of the CONNECT. Finally, in the sixth step, the resulting intervention is evidence-based, delivered through technology, and personalized for the target population. The use of IM can facilitate the identification of barriers and facilitators of the implementation of the strategy in social media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".