Evidence-Based Feasibility Analysis of Summer BSN Program Workload: A Multi-Archetype Assessment

Background

The nursing profession faces workforce shortages, with projections indicating deficits exceeding 200,000 registered nurses by 2030 (Zhang et al., 2018). Summer BSN programs have emerged as one strategy to address this shortage by enabling students to complete nursing education in compressed timeframes (Payne et al., 2020).

The catalyst for this analysis arose from difficulties experienced in synthesizing course materials across four simultaneous nursing courses. Attempts to integrate content from NCLEX_335, OBGYN_330, Adult_310, and Gerontology_315 courses revealed the substantial cognitive demands of managing multiple, concurrent educational streams. This experience, combined with similar concerns expressed by other students in the cohort, prompted a systematic examination of program workload using quantitative methods.

Statement of the Problem

Summer BSN programs compress traditional nursing curricula into intensive formats. While these programs demonstrate success through metrics such as NCLEX pass rates, limited research has quantitatively evaluated whether program demands align with human physiological and cognitive capacities. This gap is particularly relevant given that nursing students provide direct patient care during clinical rotations, where fatigue-related performance decrements carry potential consequences (Landrigan et al., 2004).

Human performance operates within defined constraints including attention span limits, working memory capacity, and physiological needs for sleep and recovery (Sweller et al., 2019). When educational demands exceed these constraints, consequences may include impaired learning, increased errors, and adverse health outcomes (van der Linden et al., 2021). This analysis examines whether the task load of a summer BSN program aligns with evidence-based human capacity limits.

Methods

Data Source

Task data were obtained from a summer BSN program spanning May 5 to August 7, 2025 (14 weeks). The dataset included 408 individual tasks across four courses: NCLEX_335 (n=57), OBGYN_330 (n=94), Adult_310 (n=127), and Gerontology_315 (n=130). Each record included course designation, date, task type, and duration when specified.

Duration Estimation Framework

Of 408 tasks, 130 (31.9%) included specified durations totaling 317.5 hours. These durations were preserved without modification. For the remaining 278 tasks (68.1%), evidence-based time estimates were applied as shown in Table 1.

Table 1: Evidence-Based Duration Estimates for Unspecified Tasks

Student Archetype Model

Four student archetypes were modeled based on cognitive psychology and nursing education research (Table 2).

Table 2: Student Archetype Characteristics and Time Multipliers

Physiological Thresholds

Two evidence-based weekly workload thresholds were established as shown in Figure 1:

The cognitive health limit (63.3 hours/week) represents sustainable workload allowing adequate sleep and self-care. The physiological maximum (84 hours/week) represents the absolute upper limit before basic human needs are compromised.

Statistical Analysis

Monte Carlo simulation (10,000 iterations) modeled workload distributions for each archetype using log-normal distributions (μ=4.331, σ=0.194) based on cognitive performance literature (Rayner et al., 2016).

Results

Overview

Analysis of 408 tasks across 14 weeks revealed substantial variation in workload requirements across student archetypes (Figure 2).

Task Distribution

The program contains 408 tasks over 96 days (14 weeks) with the following distribution:

Table 3: Weekly Task Distribution

Mean: 29.1 tasks/week (SD=11.7, Range: 8-55)

Task Type Analysis

Table 4: Task Distribution by Type with Duration Coverage

*Estimated based on evidence-based framework

Workload Analysis by Archetype

Table 5: Weekly Workload Hours by Student Archetype

Peak Week Analysis

Week 1 contains 55 tasks, representing the highest density:

Table 6: Week 1 Workload by Archetype (Hours)

Discussion

This analysis provides quantitative evidence regarding workload distribution in a summer BSN program. The finding that 100% of modeled students exceed cognitive health limits (63.3 hours/week) and 80% exceed physiological maximum limits (84 hours/week) aligns with research on sustainable performance thresholds (Stimpfel et al., 2019).

The compression of 408 tasks into 14 weeks creates an average density of 29.1 tasks/week, with substantial variation (SD=11.7). This variability, particularly the 55-task peak in week 1, suggests uneven workload distribution that may contribute to student difficulties in managing multiple concurrent courses.

Working memory capacity limits of 4±1 items (Cowan, 2001) provide context for understanding challenges in managing multiple simultaneous courses. With four concurrent courses and an average of 4.25 tasks/day, students operate at or above cognitive capacity limits, necessitating constant task-switching with associated performance costs (Monsell, 2003).

The 20 clinical shifts representing 200 hours of direct patient care warrant particular attention. Clinical intensity peaks during weeks 6-10, coinciding with continued didactic coursework. Research demonstrates that fatigue increases medical errors (Landrigan et al., 2004), suggesting implications for both educational quality and patient safety when students operate under high workload conditions.

The substantial video content (104 videos) and reading requirements (108 chapters) represent core educational components that cannot be abbreviated without compromising learning objectives. These elements alone require approximately 165 hours of engagement, or 11.8 hours per week.

Limitations

1. Archetype distributions are based on general nursing education literature and may not reflect this specific program's student population.
2. Individual variation within archetypes is not fully captured by the model.
3. Some tasks may integrate or overlap, potentially reducing total time through efficiencies not modeled.
4. Time estimates for unspecified durations, while evidence-based, remain estimates subject to variation.
5. Analysis focuses on a single semester, limiting generalizability to other program iterations.

Conclusions

This analysis quantifies workload distribution in a summer BSN program using evidence-based methodologies. Results indicate that no modeled student population can complete the program within cognitive health limits, with 80% exceeding physiological maximum capacity. The program structure creates conditions where students must choose between adequate sleep, proper nutrition, and academic task completion. These findings demonstrate that the program exceeds human physiological limits for the majority of students, with potential implications for both educational outcomes and patient safety during clinical rotations.

References

Total Reading Tasks: 108 chapters
Tasks with specified duration: 8 chapters (7.4%)
Tasks requiring estimation: 100 chapters (92.6%)

Evidence-based reading rate for medical texts: 30 pages/hour (Klatt & Klatt, 2011)
Average nursing textbook chapter: 37.5 pages
Time per chapter: 37.5 pages ÷ 30 pages/hour = 1.25 hours

Total reading time estimate:
- Specified durations: 8 chapters (actual durations used)
- Estimated durations: 100 chapters × 1.25 hours = 125 hours
- Total: 125 hours + specified durations
                

Total Video Tasks: 104 videos
Videos with specified duration: 37 (35.6%)
Videos requiring estimation: 67 (64.4%)

Specified video durations (hours:minutes):
Adult_310: 45 videos total
- Clinical SBAR videos (12): 30 minutes each = 6 hours
- Pre-class videos (5): 17:35, 16:37, 17:50, 48:48, 38:08
- Skills demonstrations (17): 3:13 to 15:43
- Other specified (11): Various durations

Gerontology_315: 30 videos total
- Chapter recordings (27): 10:14 to 96:49
- One-Minute Nurse (3): Not specified, estimated at 5 minutes each

OBGYN_330: 29 videos total
- Osmosis videos (20): 4:00 to 10:00
- Module videos (3): 109:00, 57:00, 91:00
- Other (6): Various or unspecified

Total specified duration: 30.8 hours
Average for specified videos: 30.8 hours ÷ 37 videos = 0.83 hours/video

Estimation for unspecified videos:
- One-Minute Nurse series (10): 0.25 hours each = 2.5 hours
- Osmosis videos without duration (20): 0.50 hours each = 10 hours
- Other unspecified (37): Use average of 0.50 hours = 18.5 hours
Total estimated: 31 hours

Combined total: 30.8 + 31 = 61.8 hours of video content
                

Distribution: Log-normal
Parameters: μ = 4.331, σ = 0.194
Iterations: 10,000 per archetype
Random seed: 42 (for reproducibility)

Validation metrics:
- Kolmogorov-Smirnov test for distribution fit
- Bootstrap confidence intervals (95%)
- Sensitivity analysis on multiplier variations (±10%)