Designing a framework for the evaluation of paediatric telepsychiatry: a participatory approach
Bibliographic record
Abstract
While there is a great deal of interest in evaluating participants' experiences of teleconsultation programmes, specific frameworks for such evaluations are scarce. We have conducted a multi-stage consultation to develop a framework for the study of a paediatric telepsychiatry programme. Emphasis was placed on ensuring the participation of stakeholders in the design and response stage of the evaluation. A three-part approach was taken that comprised an opinion scan, focus groups and individual interviews. This resulted in the identification of specific areas of enquiry for the evaluation. One of the key points to emerge was that attending to context is vital. In the case of telepsychiatry, it is critical to understand the nuances of the local community for whom consultations are being provided. This involves considering the 'social ecology' of each evaluation site. The evaluation should take the form of a dialogue between the evaluators and those being evaluated, in order to maximize the uptake and integration of its findings. The framework we have developed should be viewed as a guide that is general enough to be used in the design of many different types of telepsychiatry programme.
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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 | 0.330 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".