Information needs of case managers caring for persons living with HIV
Bibliographic record
Abstract
OBJECTIVE: The goals of this study were to explore the information needs of case managers who provide services to persons living with HIV (PLWH) and to assess the applicability of the Information Needs Event Taxonomy in a new population. DESIGN: The study design was observational with data collection via an online survey. MEASUREMENTS: Responses to open-ended survey questions about the information needs of case managers (n=94) related to PLWH of three levels of care complexity were categorized using the Information Needs Event Taxonomy. RESULTS: The most frequently identified needs were related to patient education resources (33%), patient data (23%), and referral resources (22%) accounting for 79% of all (N=282) information needs. LIMITATIONS: Study limitations include selection bias, recall bias, and a relatively narrow focus of the study on case-manager information needs in the context of caring for PLWH. CONCLUSION: The study findings contribute to the evidence base regarding information needs in the context of patient interactions by: (1) supporting the applicability of the Information Needs Event Taxonomy and extending it through addition of a new generic question; (2) providing a foundation for the addition of context-specific links to external information resources within information systems; (3) applying a new approach for elicitation of information needs; and (4) expanding the literature regarding addressing information needs in community-based settings for HIV services.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".