Barriers and Facilitators Influencing Call Center Nurses' Decision Support for Callers Facing Values‐Sensitive Decisions: A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Call center nurses triage symptoms and provide health information. However, information alone is not adequate for people facing values-sensitive health decisions. For these decisions, effective interventions are evidence-based patient decision aids and in-person nurse coaching using a structured process. Little is known about the quality of decision support provided by call center nurses. AIMS: To identify the barriers and facilitators influencing the provision of decision support by call center nurses to callers facing values-sensitive health decisions at a Canadian province-wide health call center. METHODS: A mixed qualitative and quantitative descriptive study from December 2003 to January 2004 using key informant interviews (n= 4), two focus groups (n= 7), a barriers assessment survey (n= 57), and analysis of simulated patient calls (n= 38) were carried out. Triangulation of these data was conducted using a conceptual content analysis method. RESULTS: Participants indicated positive attitudes toward call center nurses preparing callers facing values-sensitive decisions. Facilitators included decision support resources, nurses' ability to recognize callers having difficulty, and having a supportive organizational infrastructure. The most frequently identified barriers were (a) limited usability of patient decision aids via telephone; (b) lack of a structured process to guide nurses during these types of calls; (c) nurses' inadequate knowledge, skills, and confidence in providing values-sensitive decision support; (d) unclear program direction; (e) organizational pressure to minimize call length; and (f) low public awareness of the services. CONCLUSIONS AND IMPLICATIONS: Despite call center nurses having positive attitudes, several modifiable barriers were interfering with nurses' current approaches to supporting callers facing values-sensitive decisions. Nurses wanted educational opportunities to further develop their decision support knowledge and skills, and decision support resources that are easier to use via telephone. As well, changes to organizational policies that address identified barriers could further facilitate the provision of decision support.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".