Treatment effectiveness research in audiological rehabilitation: fundamental issues related to dependent variables
Bibliographic record
Abstract
This article addresses one specific aspect of treatment evaluation research, namely the dependent variables used to evaluate the effectiveness of treatment programs provided in audiological rehabilitation. First, some underlying principles that should guide the design of all treatment evaluation research are presented. Second, issues specifically related to the selection of outcome measures are discussed. It is argued that treatment effectiveness research should incorporate individualized outcome measures. That is, a unique set of outcome measures should be identified for each of the persons who participate in the treatment program. Further, each outcome measure should make it possible to document changes related to each participant's involvement in the specific activity identified in the objective of the intervention program. Finally, other types of dependent variables that should be considered when conducting treatment effectiveness research are discussed. Specifically, information should be collected to identify all the factors that either facilitated or constituted an obstacle to the implementation of the treatment program or the successful attainment of the targeted goal of the program. Also, information should be obtained to describe the impacts and consequences of the intervention program.
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 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 | 0.287 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".