Original research · 2026-07 edition

AI SEO Statistics: Short Term Rehab Center (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in short term rehab center.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My mom just had a hip replacement, what kind of facility does she need for the first few weeks of recovery?
Can we just do physical therapy at home after a stroke or is a dedicated rehab center significantly better?
How can I check the safety ratings and nurse-to-patient ratios for short-term rehab centers in my area?
Does Medicare usually cover the full cost of a 2-week stay at a skilled nursing facility?
What is the average out-of-pocket daily cost for a private room in a post-acute rehab center?
What is the main difference between an inpatient rehab facility and a skilled nursing facility for someone recovering from surgery?
How do I find a short-term rehab center that specializes specifically in cardiac recovery and heart health?
What are the major red flags I should look for when touring a potential rehab center for a family member?
Show all 15 questions
The hospital is discharging my dad tomorrow and says he needs rehab, how do I choose a quality place on such short notice?
My husband has Parkinson's and needs intensive physical therapy after a bad fall, what specific features should I look for in a facility?
Do most short-term rehab centers allow family pets to visit during the recovery process?
What specific questions should I ask the admissions director about their patient success rates for regaining mobility?
Is transportation provided by the rehab center if a patient needs to see their primary surgeon for a follow-up appointment?
Our insurance only approved 10 days of rehab but the doctor recommends 21, what steps can I take to get more time covered?
Do short-term rehab centers typically have a doctor on-site 24/7 or is it mostly managed by nursing staff?

Model by model

20-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about short term rehab center buyers.

Behavior rates across 15 short term rehab center buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional47%33%27%67%
Suggests DIY first33%20%27%87%
Names specific providers0%7%0%93%
Gives price or cost info13%7%13%93%
Tells to check reviews13%27%20%60%
Tells to verify credentials13%27%20%73%
Mentions case studies / portfolio7%7%0%93%
Mentions local proximity7%33%13%60%
Gives selection criteria33%67%40%33%
Warns about red flags13%13%13%73%
Asks a clarifying question60%40%0%13%
Recommends multiple quotes7%7%0%87%

By model

How each assistant handled Short Term Rehab Center questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same short term rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 26.7% (Gemini), a 20-point gap on an identical question set.

Across the 15 short term rehab center answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 512 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 short term rehab center answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 short term rehab center answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 258 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a short term rehab center buyer to a professional (46.7%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 512 words on average. Specific providers were named most often by Claude (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a short term rehab center buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT) — a 60-point spread.
  • Gives selection criteria: from 33.3% (ChatGPT) to 66.7% (Claude) — a 33-point spread.
  • Mentions local proximity: from 6.7% (ChatGPT) to 33.3% (Claude) — a 27-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 46.7% (ChatGPT) — a 20-point spread.
  • Tells the buyer to check reviews: from 13.3% (ChatGPT) to 26.7% (Claude) — a 13-point spread.

The widest single gap — asks a clarifying question, 60 points — means a short term rehab center buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the short term rehab center market.

Where they agree

The points of near-consensus in Short Term Rehab Center.

On other behaviors the three models move almost in lockstep — the points of near-consensus for short term rehab center, where all three landed within a few points of each other:

  • Warns about red flags or scams: 13.3% across all three models.
  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

All twelve coded behaviors for Short Term Rehab Center, averaged across the three models.

The behaviors AI models reproduce most often for short term rehab center are gives selection criteria (46.7% on average), recommends hiring a professional (35.6%) and asks a clarifying question (33.3%); the rarest are names a specific provider (2.2%), recommends multiple quotes (4.5%) and mentions case studies or portfolio (4.5%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 46.7% on average (ChatGPT 33.3%, Claude 66.7%, Gemini 40%) — a 33-point spread.
  • Recommends hiring a professional: 35.6% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 26.7%) — a 20-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 60%, Claude 40%, Gemini 0%) — a 60-point spread.
  • Suggests a DIY approach first: 26.7% on average (ChatGPT 33.3%, Claude 20%, Gemini 26.7%) — a 13-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
  • Mentions local proximity: 17.8% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 13.3%) — a 27-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Gives price or cost information: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Mentions case studies or portfolio: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the short term rehab center buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the short term rehab center buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 4.5%. The single least-reproduced protective signal for short term rehab center is "recommends multiple quotes" at 4.5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Short Term Rehab Center providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 short term rehab center answers, a specific provider was named in 2.2% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for short term rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Short Term Rehab Center questions cover.

The 15 questions behind every percentage on this page were drawn from real short term rehab center (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact short term rehab center question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific short term rehab center question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →