The Five Questions of C1.01: Documenting Faculty Effectiveness

Standard C1.01 of the Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) Standards, Sixth Edition, asks PA programs to answer five self-study questions about program effectiveness — and, for the first time, to decide for themselves which data to analyze and what conclusions to draw. This article walks through all five questions, with particular attention to the one that catches programs off-guard: documenting faculty effectiveness outside of teaching.

Written to the ARC-PA Standards, Sixth Edition (effective September 1, 2025). Last reviewed July 2026.

What are the five questions of C1.01?

C1.01 requires a program's ongoing self-assessment to answer five questions: (1) Are program faculty effective in operating the program outside of teaching? (2) Is the admissions process effective in selecting students who can successfully complete the program? (3) Is the didactic curriculum effective in preparing graduates for clinical practice? (4) Is the clinical curriculum effective in preparing graduates for clinical practice? (5) Overall, does the program successfully prepare graduates for clinical practice?

Each question stands on its own. Data can overlap between them, but every time you answer one, ask what is specific about this question that is different from the others — and evaluate it through its own lens. A cookie-cutter analysis repeated five times is exactly what an experienced reviewer notices.

What does C1.01 ask you to do differently under the Sixth Edition?

The Sixth Edition trades the Fifth Edition's rigidity for judgment. The structured Appendix 14 template is gone; programs now choose their data sets (the templates require some and allow additional ones), set a benchmark for every data set, and defend their own conclusions. The writing burden shrank — the data burden did not.

Three shifts matter most:

You define the ANI. ARC-PA's materials do not define what constitutes an "area needing improvement." Your program must write its own definition and apply it identically across all five questions, every cycle. A workable definition: three or more aligned data sets below benchmark, or a sustained decline in program outcomes. The "or" is new flexibility — a precipitous drop in a critical metric can warrant an ANI even without three-data-set triangulation, and a declining trend that is still above benchmark deserves attention rather than automatic dismissal.

Critical analysis got more specific. The Sixth Edition's framing rests on four concepts: comparison of data sets side by side (a course's grades against the PACKRAT organ system it teaches, for example), trends over time, triangulation across three or more aligned data sets, and contextualization — tracing contributing factors rather than reading each number in isolation.

Qualitative data is effectively required. Feedback from unsuccessful PANCE takers, competency-aligned exit-survey responses, and similar qualitative sources are now part of what makes an analysis complete. If your program only has quantitative data structured and accessible, the analysis is incomplete before it starts.

How do you document faculty effectiveness outside of teaching (Question 1)?

Question 1 asks whether principal faculty, the program director, and the medical director are effective in operating the program beyond the classroom — admissions work, academic counseling, remediation, advising, and the medical director's advocacy and curricular oversight. It is the question programs scramble on, because almost none of the standard data sets speak to it directly.

Admissions data, attrition, PANCE results, course grades, course evaluations — these were built to measure other dimensions of the program. The challenge is usually not interpreting the data; it is identifying meaningful evidence in the first place. Documenting Question 1 means building instruments for it, in advance:

• Start with the people who observe the work: Program Director evaluations. It can seem almost too simple, but the program director has firsthand knowledge of faculty involvement in admissions decisions, committee work, assessment activities, student support, remediation, and accreditation preparation. A PD evaluation should not stand alone — but it is a legitimate anchor for the evidence set.
• Build faculty-effectiveness surveys around the defined, non-teaching roles (the faculty responsibilities enumerated in the administration standards — for example the A2.05 role list — are a practical starting frame for survey items), and decide who evaluates each function: students often cannot see admissions work or curricular oversight, so many items belong to faculty peers or the program director.
• Triangulate with the other perspectives available: faculty self-assessments (context, not objective measures), administrative evaluations from chairs and deans, and documentation of committee, service, and accreditation work. Advising, mentoring, and remediation activity is non-teaching work faculty can be evaluated on directly.
• Integrate the instruments into the ongoing assessment workflow, not just pre-review preparation. Programs that reconstruct this evidence at submission time rarely produce something as defensible as a system that was running all along.

No single measure answers Question 1 completely — and it does not need to. The goal is not a perfect metric; it is reasonable evidence, applied consistently, analyzed thoughtfully, and a conclusion you can explain and support. Defensible is better than perfect. Question 1 is not technically harder than the other four; it rewards programs that built the system before they needed it.

What data should you use for Questions 2 through 5?

Start every question with its required data sets, reviewed at both the macro and micro level, then move outward to data with a genuine conceptual link to the question. One or two below-benchmark courses among twenty over three years is probably not a program-level problem — but annotate it.

Question 2 (admissions). Admissions variables are famously weak predictors of student success, so read converging signals instead: attrition, clusters of low grades early in the program, unusual remediation volume, and exit-survey items that speak to selection. The question underneath: are you selecting students who will complete the program?

Question 3 (didactic). Stay inside the didactic phase — don't reach for clinical-year data first. Courses below benchmark two years running are fertile ground, especially when tied to an organ system or task area. And think on both sides of the coin: heavy remediation concentrated in one course points at the course as much as the students.

Question 4 (clinical). Alignment between end-of-curriculum composite scores and PANCE, failed end-of-rotation exams by discipline, and the summative exam are the workhorses. Preceptor evaluations aggregate too heavily to surface much; EOR sub-scores are a rabbit hole unless a trend elsewhere sends you there. Clinical effectiveness extends past knowledge to reasoning, diagnostic, and technical skills.

Question 5 (overall effectiveness). The 10,000-foot view. PANCE and the summative are required; crosswalk them, then widen out. One weak area doesn't indict a program — widespread converging signals do.

A caution that applies to all five: don't let first-time PANCE pass rate run the analysis. A below-benchmark pass rate is a signal to investigate, not an automatic verdict — performance is a continuum that begins long before the exam.

When should you declare an ANI — and when shouldn't you?

Declare an ANI when your program's written definition is met: aligned below-benchmark data sets, a sustained decline, or a precipitous drop in a critical outcome. A single below-benchmark data point among the three to five you analyze is a signal to pause and triangulate, not an automatic finding.

The calibration cuts both ways. Declaring an ANI on most or all five questions usually means the analysis biased toward finding problems, not that it was more rigorous — and it is entirely legitimate to work through all five questions and declare none, if the data supports that. What reviewers respond to is internal consistency in how determinations were made, not the count of ANIs declared. If everything is an ANI, you're probably not going in the right direction.

How do you write the C1.01 narrative in under 1,500 words?

The 1,500-word limit is ARC-PA's own cap on the analysis narrative, not a style preference. Prioritize analysis over raw data — the data already lives in your templates. A skeleton that reliably comes in under it:

1. Overview of process — how the program approached the question, which data sets it chose and why.
2. Data-point synopses — one short paragraph per chosen data set, highlighting only what matters ("below benchmark two consecutive years," "above benchmark following two years").
3. Comparisons, trends, and triangulation — the summation that connects the data and states the conclusion: compliant, or an ANI with its action plan.

Write it, count it, compress it. If you're reproducing charts or raw numbers in the narrative, you're going in the wrong direction.

Frequently asked questions

Does ARC-PA define what an "area needing improvement" is? No. ARC-PA's materials do not define an ANI — each program must write its own definition and apply it consistently across all five C1.01 questions and every reporting cycle. A common working definition: three or more aligned data sets below benchmark, or a sustained decline in program outcomes.

Can a program answer all five C1.01 questions without declaring any ANI? Yes. If the chosen data sets sit above benchmark and triangulation surfaces no converging concerns, "compliant" across all five questions is a legitimate, defensible outcome. Reviewers evaluate the consistency of the analysis, not the number of ANIs declared.

What data is required for Question 1 (faculty effectiveness outside of teaching)? Few standard data sets connect to it directly, which is why programs build purpose-designed surveys — completed by students, faculty, and the program director — around the defined non-teaching roles, and supplement them with adjacent evidence like remediation practice and admissions outcomes.

How long should the C1.01 narrative be? ARC-PA limits the analysis narrative to 1,500 words. Programs stay under the cap by presenting the process overview, brief synopses of each data point, and the triangulated conclusion — rather than reproducing raw data already in the templates.

How often should a program work through the five questions? Annually. The templates hold five to six years of data; treated as an annual program-evaluation engine, they turn the self-study from a once-per-cycle scramble into a running record of trends against benchmarks.

Want to see this process worked on screen? Watch the free replay of our C1.01 working session — ARC-PA C1.01: When to Choose Compliant vs. ANI (All 5 Questions) — with chapter navigation, written summaries, and the full transcript.