Bibliographic record
Abstract
Implementing telehealth applications represents a substantial investment of resources, which is one reason why success is of great interest. Many research and evaluation studies have investigated measures of successful telehealth systems. However, the term "telehealth" represents a wide range of variables including clinical application, characteristics of the information being transmitted, temporal relationships of data transfer and the organizational context. These sources of variability pose many challenges for evaluation as well as for building a cumulative history of research. A conceptual framework is required that assists in categorizing results and drawing conclusions based on an accumulation of findings. One measure of "success" in health care is quality patient care and this reflects a primary reason for ICT investments. For this reason, Donabedian's work in evaluating quality provides the basis for the proposed framework. DeLone and McLean's definitions of IS success assist in conceptualizing Donabedian's structure-outcome-process variables in a telehealth context. Multiple evaluation approaches have been used to address different types of questions. Prior to the technologies being introduced to clinical care, there are usually many studies to demonstrate their effectiveness. Health Technology Assessment examines a broader context than the technology alone, including costs and comparing alternatives that would exist in the absence of telehealth. It considers performance measures; outcomes; summary measures, operational considerations, and other issues. Program Evaluation examines use of the technology to provide a service or deliver a program. Evaluation questions often address whether the program goals have been met and if it is operating as expected. Perhaps of greater concern than the evaluation approach taken is generalizability of findings. Recent studies have given inadequate attention to defining what is done (i.e. comparison of telehealth to most appropriate alternative), identifying the beneficiaries of telehealth (i.e. ensuring randomly selected participants take part) and what is measured (i.e. including benefits, drawbacks and side effects of telehealth). Evaluation efforts and frameworks have identified "success" factors such as technical acceptability of the system, cost/benefit/effectiveness, organizational support, satisfaction, recruitment and retention, client outcomes such as quality of life, acceptance by consumers and providers. Less is known about the relationship among these variables and whether the findings around one variable are generalizable to other settings or applications. For example, organizational support may be essential for successful provider-patient interactions via videoconference, which result in higher quality of life. A conceptual framework would assist in accumulating this type of evidence and supporting more advanced research efforts.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".